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<title>How education, generation and gender jointly structure green and radical right voting</title>
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            let tocShowing = false;
            // Allow the caller to control whether this is dismissed
            // when it is clicked (e.g. sidebar navigation supports
            // opening and closing the nav tree, so don't dismiss on click)
            const clickEl = placeholderDescriptor.dismissOnClick
              ? toggleContainer
              : toggleTitle;

            const closeToggle = () => {
              if (tocShowing) {
                toggleContainer.classList.remove("expanded");
                toggleContents.style.height = "0px";
                tocShowing = false;
              }
            };

            // Get rid of any expanded toggle if the user scrolls
            window.document.addEventListener(
              "scroll",
              throttle(() => {
                closeToggle();
              }, 50)
            );

            // Handle positioning of the toggle
            window.addEventListener(
              "resize",
              throttle(() => {
                elRect = undefined;
                positionToggle();
              }, 50)
            );

            window.addEventListener("quarto-hrChanged", () => {
              elRect = undefined;
            });

            // Process the click
            clickEl.onclick = () => {
              if (!tocShowing) {
                toggleContainer.classList.add("expanded");
                toggleContents.style.height = null;
                tocShowing = true;
              } else {
                closeToggle();
              }
            };
          });
        };

        // Converts a sidebar from a menu back to a sidebar
        const convertToSidebar = () => {
          for (const child of el.children) {
            child.style.opacity = 1;
            child.style.overflow = null;
            child.style.pointerEvents = null;
          }

          const placeholderEl = window.document.getElementById(
            placeholderDescriptor.id
          );
          if (placeholderEl) {
            placeholderEl.remove();
          }

          el.classList.remove("rollup");
        };

        if (isReaderMode()) {
          convertToMenu();
          isVisible = false;
        } else {
          // Find the top and bottom o the element that is being managed
          const elTop = el.offsetTop;
          const elBottom =
            elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight;

          if (!isVisible) {
            // If the element is current not visible reveal if there are
            // no conflicts with overlay regions
            if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) {
              convertToSidebar();
              isVisible = true;
            }
          } else {
            // If the element is visible, hide it if it conflicts with overlay regions
            // and insert a placeholder toggle (or if we're in reader mode)
            if (inHiddenRegion(elTop, elBottom, hiddenRegions)) {
              convertToMenu();
              isVisible = false;
            }
          }
        }
      }
    };
  };

  const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]');
  for (const tabEl of tabEls) {
    const id = tabEl.getAttribute("data-bs-target");
    if (id) {
      const columnEl = document.querySelector(
        `${id} .column-margin, .tabset-margin-content`
      );
      if (columnEl)
        tabEl.addEventListener("shown.bs.tab", function (event) {
          const el = event.srcElement;
          if (el) {
            const visibleCls = `${el.id}-margin-content`;
            // walk up until we find a parent tabset
            let panelTabsetEl = el.parentElement;
            while (panelTabsetEl) {
              if (panelTabsetEl.classList.contains("panel-tabset")) {
                break;
              }
              panelTabsetEl = panelTabsetEl.parentElement;
            }

            if (panelTabsetEl) {
              const prevSib = panelTabsetEl.previousElementSibling;
              if (
                prevSib &&
                prevSib.classList.contains("tabset-margin-container")
              ) {
                const childNodes = prevSib.querySelectorAll(
                  ".tabset-margin-content"
                );
                for (const childEl of childNodes) {
                  if (childEl.classList.contains(visibleCls)) {
                    childEl.classList.remove("collapse");
                  } else {
                    childEl.classList.add("collapse");
                  }
                }
              }
            }
          }

          layoutMarginEls();
        });
    }
  }

  // Manage the visibility of the toc and the sidebar
  const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, {
    id: "quarto-toc-toggle",
    titleSelector: "#toc-title",
    dismissOnClick: true,
  });
  const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, {
    id: "quarto-sidebarnav-toggle",
    titleSelector: ".title",
    dismissOnClick: false,
  });
  let tocLeftScrollVisibility;
  if (leftTocEl) {
    tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, {
      id: "quarto-lefttoc-toggle",
      titleSelector: "#toc-title",
      dismissOnClick: true,
    });
  }

  // Find the first element that uses formatting in special columns
  const conflictingEls = window.document.body.querySelectorAll(
    '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]'
  );

  // Filter all the possibly conflicting elements into ones
  // the do conflict on the left or ride side
  const arrConflictingEls = Array.from(conflictingEls);
  const leftSideConflictEls = arrConflictingEls.filter((el) => {
    if (el.tagName === "ASIDE") {
      return false;
    }
    return Array.from(el.classList).find((className) => {
      return (
        className !== "column-body" &&
        className.startsWith("column-") &&
        !className.endsWith("right") &&
        !className.endsWith("container") &&
        className !== "column-margin"
      );
    });
  });
  const rightSideConflictEls = arrConflictingEls.filter((el) => {
    if (el.tagName === "ASIDE") {
      return true;
    }

    const hasMarginCaption = Array.from(el.classList).find((className) => {
      return className == "margin-caption";
    });
    if (hasMarginCaption) {
      return true;
    }

    return Array.from(el.classList).find((className) => {
      return (
        className !== "column-body" &&
        !className.endsWith("container") &&
        className.startsWith("column-") &&
        !className.endsWith("left")
      );
    });
  });

  const kOverlapPaddingSize = 10;
  function toRegions(els) {
    return els.map((el) => {
      const boundRect = el.getBoundingClientRect();
      const top =
        boundRect.top +
        document.documentElement.scrollTop -
        kOverlapPaddingSize;
      return {
        top,
        bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize,
      };
    });
  }

  let hasObserved = false;
  const visibleItemObserver = (els) => {
    let visibleElements = [...els];
    const intersectionObserver = new IntersectionObserver(
      (entries, _observer) => {
        entries.forEach((entry) => {
          if (entry.isIntersecting) {
            if (visibleElements.indexOf(entry.target) === -1) {
              visibleElements.push(entry.target);
            }
          } else {
            visibleElements = visibleElements.filter((visibleEntry) => {
              return visibleEntry !== entry;
            });
          }
        });

        if (!hasObserved) {
          hideOverlappedSidebars();
        }
        hasObserved = true;
      },
      {}
    );
    els.forEach((el) => {
      intersectionObserver.observe(el);
    });

    return {
      getVisibleEntries: () => {
        return visibleElements;
      },
    };
  };

  const rightElementObserver = visibleItemObserver(rightSideConflictEls);
  const leftElementObserver = visibleItemObserver(leftSideConflictEls);

  const hideOverlappedSidebars = () => {
    marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries()));
    sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries()));
    if (tocLeftScrollVisibility) {
      tocLeftScrollVisibility(
        toRegions(leftElementObserver.getVisibleEntries())
      );
    }
  };

  window.quartoToggleReader = () => {
    // Applies a slow class (or removes it)
    // to update the transition speed
    const slowTransition = (slow) => {
      const manageTransition = (id, slow) => {
        const el = document.getElementById(id);
        if (el) {
          if (slow) {
            el.classList.add("slow");
          } else {
            el.classList.remove("slow");
          }
        }
      };

      manageTransition("TOC", slow);
      manageTransition("quarto-sidebar", slow);
    };
    const readerMode = !isReaderMode();
    setReaderModeValue(readerMode);

    // If we're entering reader mode, slow the transition
    if (readerMode) {
      slowTransition(readerMode);
    }
    highlightReaderToggle(readerMode);
    hideOverlappedSidebars();

    // If we're exiting reader mode, restore the non-slow transition
    if (!readerMode) {
      slowTransition(!readerMode);
    }
  };

  const highlightReaderToggle = (readerMode) => {
    const els = document.querySelectorAll(".quarto-reader-toggle");
    if (els) {
      els.forEach((el) => {
        if (readerMode) {
          el.classList.add("reader");
        } else {
          el.classList.remove("reader");
        }
      });
    }
  };

  const setReaderModeValue = (val) => {
    if (window.location.protocol !== "file:") {
      window.localStorage.setItem("quarto-reader-mode", val);
    } else {
      localReaderMode = val;
    }
  };

  const isReaderMode = () => {
    if (window.location.protocol !== "file:") {
      return window.localStorage.getItem("quarto-reader-mode") === "true";
    } else {
      return localReaderMode;
    }
  };
  let localReaderMode = null;

  const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded");
  const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1;

  // Walk the TOC and collapse/expand nodes
  // Nodes are expanded if:
  // - they are top level
  // - they have children that are 'active' links
  // - they are directly below an link that is 'active'
  const walk = (el, depth) => {
    // Tick depth when we enter a UL
    if (el.tagName === "UL") {
      depth = depth + 1;
    }

    // It this is active link
    let isActiveNode = false;
    if (el.tagName === "A" && el.classList.contains("active")) {
      isActiveNode = true;
    }

    // See if there is an active child to this element
    let hasActiveChild = false;
    for (child of el.children) {
      hasActiveChild = walk(child, depth) || hasActiveChild;
    }

    // Process the collapse state if this is an UL
    if (el.tagName === "UL") {
      if (tocOpenDepth === -1 && depth > 1) {
        // toc-expand: false
        el.classList.add("collapse");
      } else if (
        depth <= tocOpenDepth ||
        hasActiveChild ||
        prevSiblingIsActiveLink(el)
      ) {
        el.classList.remove("collapse");
      } else {
        el.classList.add("collapse");
      }

      // untick depth when we leave a UL
      depth = depth - 1;
    }
    return hasActiveChild || isActiveNode;
  };

  // walk the TOC and expand / collapse any items that should be shown
  if (tocEl) {
    updateActiveLink();
    walk(tocEl, 0);
  }

  // Throttle the scroll event and walk peridiocally
  window.document.addEventListener(
    "scroll",
    throttle(() => {
      if (tocEl) {
        updateActiveLink();
        walk(tocEl, 0);
      }
      if (!isReaderMode()) {
        hideOverlappedSidebars();
      }
    }, 5)
  );
  window.addEventListener(
    "resize",
    throttle(() => {
      if (tocEl) {
        updateActiveLink();
        walk(tocEl, 0);
      }
      if (!isReaderMode()) {
        hideOverlappedSidebars();
      }
    }, 10)
  );
  hideOverlappedSidebars();
  highlightReaderToggle(isReaderMode());
});

// grouped tabsets
window.addEventListener("pageshow", (_event) => {
  function getTabSettings() {
    const data = localStorage.getItem("quarto-persistent-tabsets-data");
    if (!data) {
      localStorage.setItem("quarto-persistent-tabsets-data", "{}");
      return {};
    }
    if (data) {
      return JSON.parse(data);
    }
  }

  function setTabSettings(data) {
    localStorage.setItem(
      "quarto-persistent-tabsets-data",
      JSON.stringify(data)
    );
  }

  function setTabState(groupName, groupValue) {
    const data = getTabSettings();
    data[groupName] = groupValue;
    setTabSettings(data);
  }

  function toggleTab(tab, active) {
    const tabPanelId = tab.getAttribute("aria-controls");
    const tabPanel = document.getElementById(tabPanelId);
    if (active) {
      tab.classList.add("active");
      tabPanel.classList.add("active");
    } else {
      tab.classList.remove("active");
      tabPanel.classList.remove("active");
    }
  }

  function toggleAll(selectedGroup, selectorsToSync) {
    for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) {
      const active = selectedGroup === thisGroup;
      for (const tab of tabs) {
        toggleTab(tab, active);
      }
    }
  }

  function findSelectorsToSyncByLanguage() {
    const result = {};
    const tabs = Array.from(
      document.querySelectorAll(`div[data-group] a[id^='tabset-']`)
    );
    for (const item of tabs) {
      const div = item.parentElement.parentElement.parentElement;
      const group = div.getAttribute("data-group");
      if (!result[group]) {
        result[group] = {};
      }
      const selectorsToSync = result[group];
      const value = item.innerHTML;
      if (!selectorsToSync[value]) {
        selectorsToSync[value] = [];
      }
      selectorsToSync[value].push(item);
    }
    return result;
  }

  function setupSelectorSync() {
    const selectorsToSync = findSelectorsToSyncByLanguage();
    Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => {
      Object.entries(tabSetsByValue).forEach(([value, items]) => {
        items.forEach((item) => {
          item.addEventListener("click", (_event) => {
            setTabState(group, value);
            toggleAll(value, selectorsToSync[group]);
          });
        });
      });
    });
    return selectorsToSync;
  }

  const selectorsToSync = setupSelectorSync();
  for (const [group, selectedName] of Object.entries(getTabSettings())) {
    const selectors = selectorsToSync[group];
    // it's possible that stale state gives us empty selections, so we explicitly check here.
    if (selectors) {
      toggleAll(selectedName, selectors);
    }
  }
});

function throttle(func, wait) {
  let waiting = false;
  return function () {
    if (!waiting) {
      func.apply(this, arguments);
      waiting = true;
      setTimeout(function () {
        waiting = false;
      }, wait);
    }
  };
}

function nexttick(func) {
  return setTimeout(func, 0);
}
</script>
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<script>/*!
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m?m(Object.assign({},e.rects,{placement:e.placement})):m,O="number"==typeof C?{mainAxis:C,altAxis:C}:Object.assign({mainAxis:0,altAxis:0},C),x=e.modifiersData.offset?e.modifiersData.offset[e.placement]:null,k={x:0,y:0};if(A){if(o){var L,S="y"===y?zt:Vt,D="y"===y?Rt:qt,$="y"===y?"height":"width",I=A[y],N=I+g[S],P=I-g[D],M=f?-T[$]/2:0,j=b===Xt?E[$]:T[$],F=b===Xt?-T[$]:-E[$],H=e.elements.arrow,W=f&&H?Ce(H):{width:0,height:0},B=e.modifiersData["arrow#persistent"]?e.modifiersData["arrow#persistent"].padding:{top:0,right:0,bottom:0,left:0},z=B[S],R=B[D],q=Ne(0,E[$],W[$]),V=v?E[$]/2-M-q-z-O.mainAxis:j-q-z-O.mainAxis,K=v?-E[$]/2+M+q+R+O.mainAxis:F+q+R+O.mainAxis,Q=e.elements.arrow&&$e(e.elements.arrow),X=Q?"y"===y?Q.clientTop||0:Q.clientLeft||0:0,Y=null!=(L=null==x?void 0:x[y])?L:0,U=I+K-Y,G=Ne(f?ye(N,I+V-Y-X):N,I,f?ve(P,U):P);A[y]=G,k[y]=G-I}if(a){var J,Z="x"===y?zt:Vt,tt="x"===y?Rt:qt,et=A[w],it="y"===w?"height":"width",nt=et+g[Z],st=et-g[tt],ot=-1!==[zt,Vt].indexOf(_),rt=null!=(J=null==x?void 0:x[w])?J:0,at=ot?nt:et-E[it]-T[it]-rt+O.altAxis,lt=ot?et+E[it]+T[it]-rt-O.altAxis:st,ct=f&&ot?function(t,e,i){var n=Ne(t,e,i);return n>i?i:n}(at,et,lt):Ne(f?at:nt,et,f?lt:st);A[w]=ct,k[w]=ct-et}e.modifiersData[n]=k}},requiresIfExists:["offset"]};function di(t,e,i){void 0===i&&(i=!1);var n,s,o=me(e),r=me(e)&&function(t){var e=t.getBoundingClientRect(),i=we(e.width)/t.offsetWidth||1,n=we(e.height)/t.offsetHeight||1;return 1!==i||1!==n}(e),a=Le(e),l=Te(t,r,i),c={scrollLeft:0,scrollTop:0},h={x:0,y:0};return(o||!o&&!i)&&(("body"!==ue(e)||Ue(a))&&(c=(n=e)!==fe(n)&&me(n)?{scrollLeft:(s=n).scrollLeft,scrollTop:s.scrollTop}:Xe(n)),me(e)?((h=Te(e,!0)).x+=e.clientLeft,h.y+=e.clientTop):a&&(h.x=Ye(a))),{x:l.left+c.scrollLeft-h.x,y:l.top+c.scrollTop-h.y,width:l.width,height:l.height}}function ui(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var fi={placement:"bottom",modifiers:[],strategy:"absolute"};function pi(){for(var t=arguments.length,e=new Array(t),i=0;i<t;i++)e[i]=arguments[i];return!e.some((function(t){return!(t&&"function"==typeof t.getBoundingClientRect)}))}function mi(t){void 0===t&&(t={});var e=t,i=e.defaultModifiers,n=void 0===i?[]:i,s=e.defaultOptions,o=void 0===s?fi:s;return function(t,e,i){void 0===i&&(i=o);var s,r,a={placement:"bottom",orderedModifiers:[],options:Object.assign({},fi,o),modifiersData:{},elements:{reference:t,popper:e},attributes:{},styles:{}},l=[],c=!1,h={state:a,setOptions:function(i){var s="function"==typeof i?i(a.options):i;d(),a.options=Object.assign({},o,a.options,s),a.scrollParents={reference:pe(t)?Je(t):t.contextElement?Je(t.contextElement):[],popper:Je(e)};var r,c,u=function(t){var e=ui(t);return de.reduce((function(t,i){return t.concat(e.filter((function(t){return t.phase===i})))}),[])}((r=[].concat(n,a.options.modifiers),c=r.reduce((function(t,e){var i=t[e.name];return t[e.name]=i?Object.assign({},i,e,{options:Object.assign({},i.options,e.options),data:Object.assign({},i.data,e.data)}):e,t}),{}),Object.keys(c).map((function(t){return c[t]}))));return a.orderedModifiers=u.filter((function(t){return t.enabled})),a.orderedModifiers.forEach((function(t){var e=t.name,i=t.options,n=void 0===i?{}:i,s=t.effect;if("function"==typeof s){var o=s({state:a,name:e,instance:h,options:n});l.push(o||function(){})}})),h.update()},forceUpdate:function(){if(!c){var t=a.elements,e=t.reference,i=t.popper;if(pi(e,i)){a.rects={reference:di(e,$e(i),"fixed"===a.options.strategy),popper:Ce(i)},a.reset=!1,a.placement=a.options.placement,a.orderedModifiers.forEach((function(t){return a.modifiersData[t.name]=Object.assign({},t.data)}));for(var n=0;n<a.orderedModifiers.length;n++)if(!0!==a.reset){var s=a.orderedModifiers[n],o=s.fn,r=s.options,l=void 0===r?{}:r,d=s.name;"function"==typeof o&&(a=o({state:a,options:l,name:d,instance:h})||a)}else a.reset=!1,n=-1}}},update:(s=function(){return new Promise((function(t){h.forceUpdate(),t(a)}))},function(){return r||(r=new Promise((function(t){Promise.resolve().then((function(){r=void 0,t(s())}))}))),r}),destroy:function(){d(),c=!0}};if(!pi(t,e))return h;function d(){l.forEach((function(t){return t()})),l=[]}return h.setOptions(i).then((function(t){!c&&i.onFirstUpdate&&i.onFirstUpdate(t)})),h}}var gi=mi(),_i=mi({defaultModifiers:[Re,ci,Be,_e]}),bi=mi({defaultModifiers:[Re,ci,Be,_e,li,si,hi,je,ai]});const vi=Object.freeze(Object.defineProperty({__proto__:null,afterMain:ae,afterRead:se,afterWrite:he,applyStyles:_e,arrow:je,auto:Kt,basePlacements:Qt,beforeMain:oe,beforeRead:ie,beforeWrite:le,bottom:Rt,clippingParents:Ut,computeStyles:Be,createPopper:bi,createPopperBase:gi,createPopperLite:_i,detectOverflow:ii,end:Yt,eventListeners:Re,flip:si,hide:ai,left:Vt,main:re,modifierPhases:de,offset:li,placements:ee,popper:Jt,popperGenerator:mi,popperOffsets:ci,preventOverflow:hi,read:ne,reference:Zt,right:qt,start:Xt,top:zt,variationPlacements:te,viewport:Gt,write:ce},Symbol.toStringTag,{value:"Module"})),yi="dropdown",wi=".bs.dropdown",Ai=".data-api",Ei="ArrowUp",Ti="ArrowDown",Ci=`hide${wi}`,Oi=`hidden${wi}`,xi=`show${wi}`,ki=`shown${wi}`,Li=`click${wi}${Ai}`,Si=`keydown${wi}${Ai}`,Di=`keyup${wi}${Ai}`,$i="show",Ii='[data-bs-toggle="dropdown"]:not(.disabled):not(:disabled)',Ni=`${Ii}.${$i}`,Pi=".dropdown-menu",Mi=p()?"top-end":"top-start",ji=p()?"top-start":"top-end",Fi=p()?"bottom-end":"bottom-start",Hi=p()?"bottom-start":"bottom-end",Wi=p()?"left-start":"right-start",Bi=p()?"right-start":"left-start",zi={autoClose:!0,boundary:"clippingParents",display:"dynamic",offset:[0,2],popperConfig:null,reference:"toggle"},Ri={autoClose:"(boolean|string)",boundary:"(string|element)",display:"string",offset:"(array|string|function)",popperConfig:"(null|object|function)",reference:"(string|element|object)"};class qi extends W{constructor(t,e){super(t,e),this._popper=null,this._parent=this._element.parentNode,this._menu=z.next(this._element,Pi)[0]||z.prev(this._element,Pi)[0]||z.findOne(Pi,this._parent),this._inNavbar=this._detectNavbar()}static get Default(){return zi}static get DefaultType(){return Ri}static get NAME(){return yi}toggle(){return this._isShown()?this.hide():this.show()}show(){if(l(this._element)||this._isShown())return;const t={relatedTarget:this._element};if(!N.trigger(this._element,xi,t).defaultPrevented){if(this._createPopper(),"ontouchstart"in document.documentElement&&!this._parent.closest(".navbar-nav"))for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._element.focus(),this._element.setAttribute("aria-expanded",!0),this._menu.classList.add($i),this._element.classList.add($i),N.trigger(this._element,ki,t)}}hide(){if(l(this._element)||!this._isShown())return;const t={relatedTarget:this._element};this._completeHide(t)}dispose(){this._popper&&this._popper.destroy(),super.dispose()}update(){this._inNavbar=this._detectNavbar(),this._popper&&this._popper.update()}_completeHide(t){if(!N.trigger(this._element,Ci,t).defaultPrevented){if("ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._popper&&this._popper.destroy(),this._menu.classList.remove($i),this._element.classList.remove($i),this._element.setAttribute("aria-expanded","false"),F.removeDataAttribute(this._menu,"popper"),N.trigger(this._element,Oi,t)}}_getConfig(t){if("object"==typeof(t=super._getConfig(t)).reference&&!o(t.reference)&&"function"!=typeof t.reference.getBoundingClientRect)throw new TypeError(`${yi.toUpperCase()}: Option "reference" provided type "object" without a required "getBoundingClientRect" method.`);return t}_createPopper(){if(void 0===vi)throw new TypeError("Bootstrap's dropdowns require Popper (https://popper.js.org)");let t=this._element;"parent"===this._config.reference?t=this._parent:o(this._config.reference)?t=r(this._config.reference):"object"==typeof this._config.reference&&(t=this._config.reference);const e=this._getPopperConfig();this._popper=bi(t,this._menu,e)}_isShown(){return this._menu.classList.contains($i)}_getPlacement(){const t=this._parent;if(t.classList.contains("dropend"))return Wi;if(t.classList.contains("dropstart"))return Bi;if(t.classList.contains("dropup-center"))return"top";if(t.classList.contains("dropdown-center"))return"bottom";const e="end"===getComputedStyle(this._menu).getPropertyValue("--bs-position").trim();return t.classList.contains("dropup")?e?ji:Mi:e?Hi:Fi}_detectNavbar(){return null!==this._element.closest(".navbar")}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(F.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>a(t)));i.length&&b(i,e,t===Ti,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=qi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=z.find(Ni);for(const i of e){const e=qi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"<div></div>"},Un={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Gn={entry:"(string|element|function|null)",selector:"(string|element)"};class Jn extends H{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Yn}static get DefaultType(){return Un}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'<div class="tooltip" role="tooltip"><div class="tooltip-arrow"></div><div class="tooltip-inner"></div></div>',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return bi(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return g(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...g(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)N.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===ss?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===ss?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");N.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?os:ss]=!0,e._enter()})),N.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?os:ss]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},N.on(this._element.closest(is),ns,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'<div class="popover" role="tooltip"><div class="popover-arrow"></div><h3 class="popover-header"></h3><div class="popover-body"></div></div>',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{".popover-header":this._getTitle(),".popover-body":this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=us.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(us);const fs=".bs.scrollspy",ps=`activate${fs}`,ms=`click${fs}`,gs=`load${fs}.data-api`,_s="active",bs="[href]",vs=".nav-link",ys=`${vs}, .nav-item > ${vs}, .list-group-item`,ws={offset:null,rootMargin:"0px 0px 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t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=z.find(bs,this._config.target);for(const e of t){if(!e.hash||l(e))continue;const t=z.findOne(decodeURI(e.hash),this._element);a(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(_s),this._activateParents(t),N.trigger(this._element,ps,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))z.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(_s);else for(const e of z.parents(t,".nav, .list-group"))for(const t of z.prev(e,ys))t.classList.add(_s)}_clearActiveClass(t){t.classList.remove(_s);const e=z.find(`${bs}.${_s}`,t);for(const t of e)t.classList.remove(_s)}static jQueryInterface(t){return this.each((function(){const e=Es.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}N.on(window,gs,(()=>{for(const t of z.find('[data-bs-spy="scroll"]'))Es.getOrCreateInstance(t)})),m(Es);const Ts=".bs.tab",Cs=`hide${Ts}`,Os=`hidden${Ts}`,xs=`show${Ts}`,ks=`shown${Ts}`,Ls=`click${Ts}`,Ss=`keydown${Ts}`,Ds=`load${Ts}`,$s="ArrowLeft",Is="ArrowRight",Ns="ArrowUp",Ps="ArrowDown",Ms="Home",js="End",Fs="active",Hs="fade",Ws="show",Bs=":not(.dropdown-toggle)",zs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',Rs=`.nav-link${Bs}, .list-group-item${Bs}, [role="tab"]${Bs}, ${zs}`,qs=`.${Fs}[data-bs-toggle="tab"], .${Fs}[data-bs-toggle="pill"], .${Fs}[data-bs-toggle="list"]`;class Vs extends W{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),N.on(this._element,Ss,(t=>this._keydown(t))))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?N.trigger(e,Cs,{relatedTarget:t}):null;N.trigger(t,xs,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(Fs),this._activate(z.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),N.trigger(t,ks,{relatedTarget:e})):t.classList.add(Ws)}),t,t.classList.contains(Hs)))}_deactivate(t,e){t&&(t.classList.remove(Fs),t.blur(),this._deactivate(z.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),N.trigger(t,Os,{relatedTarget:e})):t.classList.remove(Ws)}),t,t.classList.contains(Hs)))}_keydown(t){if(![$s,Is,Ns,Ps,Ms,js].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=this._getChildren().filter((t=>!l(t)));let 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) format("woff");
}
.bi::before,
[class^="bi-"]::before,
[class*=" bi-"]::before {
display: inline-block;
font-family: bootstrap-icons !important;
font-style: normal;
font-weight: normal !important;
font-variant: normal;
text-transform: none;
line-height: 1;
vertical-align: -.125em;
-webkit-font-smoothing: antialiased;
-moz-osx-font-smoothing: grayscale;
}
.bi-123::before { content: "\f67f"; }
.bi-alarm-fill::before { content: "\f101"; }
.bi-alarm::before { content: "\f102"; }
.bi-align-bottom::before { content: "\f103"; }
.bi-align-center::before { content: "\f104"; }
.bi-align-end::before { content: "\f105"; }
.bi-align-middle::before { content: "\f106"; }
.bi-align-start::before { content: "\f107"; }
.bi-align-top::before { content: "\f108"; }
.bi-alt::before { content: "\f109"; }
.bi-app-indicator::before { content: "\f10a"; }
.bi-app::before { content: "\f10b"; }
.bi-archive-fill::before { content: "\f10c"; }
.bi-archive::before { content: "\f10d"; }
.bi-arrow-90deg-down::before { content: "\f10e"; }
.bi-arrow-90deg-left::before { content: "\f10f"; }
.bi-arrow-90deg-right::before { content: "\f110"; }
.bi-arrow-90deg-up::before { content: "\f111"; }
.bi-arrow-bar-down::before { content: "\f112"; }
.bi-arrow-bar-left::before { content: "\f113"; }
.bi-arrow-bar-right::before { content: "\f114"; }
.bi-arrow-bar-up::before { content: "\f115"; }
.bi-arrow-clockwise::before { content: "\f116"; }
.bi-arrow-counterclockwise::before { content: "\f117"; }
.bi-arrow-down-circle-fill::before { content: "\f118"; }
.bi-arrow-down-circle::before { content: "\f119"; }
.bi-arrow-down-left-circle-fill::before { content: "\f11a"; }
.bi-arrow-down-left-circle::before { content: "\f11b"; }
.bi-arrow-down-left-square-fill::before { content: "\f11c"; }
.bi-arrow-down-left-square::before { content: "\f11d"; }
.bi-arrow-down-left::before { content: "\f11e"; }
.bi-arrow-down-right-circle-fill::before { content: "\f11f"; }
.bi-arrow-down-right-circle::before { content: "\f120"; }
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.bi-suitcase-fill::before { content: "\f8fd"; }
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.bi-suitcase2-fill::before { content: "\f901"; }
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.bi-vignette::before { content: "\f903"; }
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<div id="quarto-content" class="page-columns page-rows-contents page-layout-article">
<div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
  <nav id="TOC" role="doc-toc" class="toc-active">
    <h2 id="toc-title">Table of contents</h2>
   
  <ul>
  <li><a href="#preparation" id="toc-preparation" class="nav-link active" data-scroll-target="#preparation"><span class="header-section-number">1</span> Preparation</a>
  <ul class="collapse">
  <li><a href="#packages-and-appearance" id="toc-packages-and-appearance" class="nav-link" data-scroll-target="#packages-and-appearance"><span class="header-section-number">1.1</span> Packages and appearance</a></li>
  <li><a href="#data-and-subsets" id="toc-data-and-subsets" class="nav-link" data-scroll-target="#data-and-subsets"><span class="header-section-number">1.2</span> Data and subsets</a></li>
  </ul></li>
  <li><a href="#descriptive-results" id="toc-descriptive-results" class="nav-link" data-scroll-target="#descriptive-results"><span class="header-section-number">2</span> Descriptive results</a>
  <ul class="collapse">
  <li><a href="#party-strength-over-time" id="toc-party-strength-over-time" class="nav-link" data-scroll-target="#party-strength-over-time"><span class="header-section-number">2.1</span> Party strength over time</a></li>
  <li><a href="#education-by-generation-and-gender" id="toc-education-by-generation-and-gender" class="nav-link" data-scroll-target="#education-by-generation-and-gender"><span class="header-section-number">2.2</span> Education by generation and gender</a></li>
  <li><a href="#green-vote-by-generation-education-and-gender" id="toc-green-vote-by-generation-education-and-gender" class="nav-link" data-scroll-target="#green-vote-by-generation-education-and-gender"><span class="header-section-number">2.3</span> Green vote by generation, education and gender</a></li>
  <li><a href="#radical-right-vote-by-generation-education-and-gender" id="toc-radical-right-vote-by-generation-education-and-gender" class="nav-link" data-scroll-target="#radical-right-vote-by-generation-education-and-gender"><span class="header-section-number">2.4</span> Radical right vote by generation, education and gender</a></li>
  </ul></li>
  <li><a href="#green-vote" id="toc-green-vote" class="nav-link" data-scroll-target="#green-vote"><span class="header-section-number">3</span> Green vote</a>
  <ul class="collapse">
  <li><a href="#multilevel-model-voting-for-the-greens-two-way-interaction" id="toc-multilevel-model-voting-for-the-greens-two-way-interaction" class="nav-link" data-scroll-target="#multilevel-model-voting-for-the-greens-two-way-interaction"><span class="header-section-number">3.1</span> Multilevel model: Voting for the greens (two-way interaction)</a></li>
  <li><a href="#voting-for-a-green-party-education-and-ess-wave" id="toc-voting-for-a-green-party-education-and-ess-wave" class="nav-link" data-scroll-target="#voting-for-a-green-party-education-and-ess-wave"><span class="header-section-number">3.2</span> Voting for a green party – education and ESS wave</a></li>
  <li><a href="#voting-for-a-green-party-education-and-age-group" id="toc-voting-for-a-green-party-education-and-age-group" class="nav-link" data-scroll-target="#voting-for-a-green-party-education-and-age-group"><span class="header-section-number">3.3</span> Voting for a green party – education and age group</a></li>
  <li><a href="#multilevel-model-voting-for-the-greens-three-way-interaction" id="toc-multilevel-model-voting-for-the-greens-three-way-interaction" class="nav-link" data-scroll-target="#multilevel-model-voting-for-the-greens-three-way-interaction"><span class="header-section-number">3.4</span> Multilevel model: Voting for the greens (three-way interaction)</a></li>
  <li><a href="#table-with-predicted-probabilities-of-voting-for-the-greens" id="toc-table-with-predicted-probabilities-of-voting-for-the-greens" class="nav-link" data-scroll-target="#table-with-predicted-probabilities-of-voting-for-the-greens"><span class="header-section-number">3.5</span> Table with predicted probabilities of voting for the greens</a></li>
  <li><a href="#multilevel-model-voting-for-the-greens-education-ess-round-gender-three-way-interaction" id="toc-multilevel-model-voting-for-the-greens-education-ess-round-gender-three-way-interaction" class="nav-link" data-scroll-target="#multilevel-model-voting-for-the-greens-education-ess-round-gender-three-way-interaction"><span class="header-section-number">3.6</span> Multilevel model: Voting for the greens – education, ESS round &amp; gender (three-way interaction)</a></li>
  <li><a href="#multilevel-model-voting-for-the-greens-education-ess-round-age-group-three-way-interaction" id="toc-multilevel-model-voting-for-the-greens-education-ess-round-age-group-three-way-interaction" class="nav-link" data-scroll-target="#multilevel-model-voting-for-the-greens-education-ess-round-age-group-three-way-interaction"><span class="header-section-number">3.7</span> Multilevel model: Voting for the greens – education, ESS round &amp; age group (three-way interaction)</a></li>
  <li><a href="#marginal-effects-of-women-vs.-men-on-voting-for-the-greens" id="toc-marginal-effects-of-women-vs.-men-on-voting-for-the-greens" class="nav-link" data-scroll-target="#marginal-effects-of-women-vs.-men-on-voting-for-the-greens"><span class="header-section-number">3.8</span> Marginal effects of women vs. men on voting for the greens</a></li>
  <li><a href="#remove-files" id="toc-remove-files" class="nav-link" data-scroll-target="#remove-files"><span class="header-section-number">3.9</span> Remove files</a></li>
  </ul></li>
  <li><a href="#radical-right-vote" id="toc-radical-right-vote" class="nav-link" data-scroll-target="#radical-right-vote"><span class="header-section-number">4</span> Radical right vote</a>
  <ul class="collapse">
  <li><a href="#multilevel-model-voting-for-the-radical-right-two-way-interaction" id="toc-multilevel-model-voting-for-the-radical-right-two-way-interaction" class="nav-link" data-scroll-target="#multilevel-model-voting-for-the-radical-right-two-way-interaction"><span class="header-section-number">4.1</span> Multilevel model: Voting for the radical right (two-way interaction)</a></li>
  <li><a href="#voting-for-a-radical-right-party-by-education-and-ess-round" id="toc-voting-for-a-radical-right-party-by-education-and-ess-round" class="nav-link" data-scroll-target="#voting-for-a-radical-right-party-by-education-and-ess-round"><span class="header-section-number">4.2</span> Voting for a radical right party by education and ESS round</a></li>
  <li><a href="#voting-for-a-radical-right-party-by-education-and-age-group" id="toc-voting-for-a-radical-right-party-by-education-and-age-group" class="nav-link" data-scroll-target="#voting-for-a-radical-right-party-by-education-and-age-group"><span class="header-section-number">4.3</span> Voting for a radical right party by education and age group</a></li>
  <li><a href="#multilevel-model-voting-for-radical-right-parties-three-way-interaction" id="toc-multilevel-model-voting-for-radical-right-parties-three-way-interaction" class="nav-link" data-scroll-target="#multilevel-model-voting-for-radical-right-parties-three-way-interaction"><span class="header-section-number">4.4</span> Multilevel model: Voting for radical right parties (three-way interaction)</a></li>
  <li><a href="#table-with-predicted-probabilities-of-voting-for-the-radical-right" id="toc-table-with-predicted-probabilities-of-voting-for-the-radical-right" class="nav-link" data-scroll-target="#table-with-predicted-probabilities-of-voting-for-the-radical-right"><span class="header-section-number">4.5</span> Table with predicted probabilities of voting for the radical right</a></li>
  <li><a href="#predicted-probabilities-of-radical-right-by-education-ess-round-and-gender" id="toc-predicted-probabilities-of-radical-right-by-education-ess-round-and-gender" class="nav-link" data-scroll-target="#predicted-probabilities-of-radical-right-by-education-ess-round-and-gender"><span class="header-section-number">4.6</span> Predicted probabilities of radical right: by education, ESS round and gender</a></li>
  <li><a href="#predicted-probabilities-of-radical-right-by-gender-cohort-and-age-group" id="toc-predicted-probabilities-of-radical-right-by-gender-cohort-and-age-group" class="nav-link" data-scroll-target="#predicted-probabilities-of-radical-right-by-gender-cohort-and-age-group"><span class="header-section-number">4.7</span> Predicted probabilities of radical right: by gender, cohort and age group</a></li>
  <li><a href="#marginal-effects-of-women-vs.-men-on-voting-for-the-radical-right-across-cohorts-and-education-levels" id="toc-marginal-effects-of-women-vs.-men-on-voting-for-the-radical-right-across-cohorts-and-education-levels" class="nav-link" data-scroll-target="#marginal-effects-of-women-vs.-men-on-voting-for-the-radical-right-across-cohorts-and-education-levels"><span class="header-section-number">4.8</span> Marginal effects of women vs. men on voting for the radical right across cohorts and education levels</a></li>
  </ul></li>
  <li><a href="#tables" id="toc-tables" class="nav-link" data-scroll-target="#tables"><span class="header-section-number">5</span> Tables</a>
  <ul class="collapse">
  <li><a href="#preliminaries" id="toc-preliminaries" class="nav-link" data-scroll-target="#preliminaries"><span class="header-section-number">5.1</span> Preliminaries</a></li>
  <li><a href="#all-models-of-green-vote" id="toc-all-models-of-green-vote" class="nav-link" data-scroll-target="#all-models-of-green-vote"><span class="header-section-number">5.2</span> All models of green vote</a></li>
  <li><a href="#all-models-of-radical-right-vote" id="toc-all-models-of-radical-right-vote" class="nav-link" data-scroll-target="#all-models-of-radical-right-vote"><span class="header-section-number">5.3</span> All models of radical right vote</a></li>
  </ul></li>
  <li><a href="#alternative-models" id="toc-alternative-models" class="nav-link" data-scroll-target="#alternative-models"><span class="header-section-number">6</span> Alternative models</a>
  <ul class="collapse">
  <li><a href="#logistic-regression-with-country-wave-fixed-effects-and-clustered-standard-errors." id="toc-logistic-regression-with-country-wave-fixed-effects-and-clustered-standard-errors." class="nav-link" data-scroll-target="#logistic-regression-with-country-wave-fixed-effects-and-clustered-standard-errors."><span class="header-section-number">6.1</span> Logistic regression with country-wave fixed effects and clustered standard errors.</a></li>
  </ul></li>
  <li><a href="#fine-grained-cohorts" id="toc-fine-grained-cohorts" class="nav-link" data-scroll-target="#fine-grained-cohorts"><span class="header-section-number">7</span> Fine-grained cohorts</a>
  <ul class="collapse">
  <li><a href="#multilevel-model-with-three-way-interaction-voting-for-the-greens-alternative-cohorts" id="toc-multilevel-model-with-three-way-interaction-voting-for-the-greens-alternative-cohorts" class="nav-link" data-scroll-target="#multilevel-model-with-three-way-interaction-voting-for-the-greens-alternative-cohorts"><span class="header-section-number">7.1</span> Multilevel model with three-way interaction: Voting for the greens (alternative cohorts)</a></li>
  <li><a href="#multilevel-model-with-three-way-interaction-voting-for-the-radical-right-alternative-cohorts" id="toc-multilevel-model-with-three-way-interaction-voting-for-the-radical-right-alternative-cohorts" class="nav-link" data-scroll-target="#multilevel-model-with-three-way-interaction-voting-for-the-radical-right-alternative-cohorts"><span class="header-section-number">7.2</span> Multilevel model with three-way interaction: Voting for the radical right (alternative cohorts)</a></li>
  </ul></li>
  </ul>
</nav>
</div>
<main class="content" id="quarto-document-content">

<header id="title-block-header" class="quarto-title-block default">
<div class="quarto-title">
<h1 class="title">How education, generation and gender jointly structure green and radical right voting</h1>
<p class="subtitle lead">Reproduction File</p>
</div>


<div class="quarto-title-meta-author">
  <div class="quarto-title-meta-heading">Authors</div>
  <div class="quarto-title-meta-heading">Affiliation</div>
  
    <div class="quarto-title-meta-contents">
    <p class="author">Armin Schäfer <a href="mailto:schaefer@politik.uni-mainz.de" class="quarto-title-author-email"><i class="bi bi-envelope"></i></a> <a href="https://orcid.org/0000-0002-2538-0092" class="quarto-title-author-orcid"> <img role="img" src="data:image/png;base64,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"></a></p>
  </div>
  <div class="quarto-title-meta-contents">
        <p class="affiliation">
            Johannes Gutenberg University
          </p>
      </div>
    <div class="quarto-title-meta-contents">
    <p class="author">Nils D. Steiner <a href="mailto:steiner@politik.uni-mainz.de" class="quarto-title-author-email"><i class="bi bi-envelope"></i></a> <a href="https://orcid.org/0000-0003-3433-4079" class="quarto-title-author-orcid"> <img role="img" src="data:image/png;base64,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"></a></p>
  </div>
  <div class="quarto-title-meta-contents">
        <p class="affiliation">
            Johannes Gutenberg University
          </p>
      </div>
  </div>

<div class="quarto-title-meta">

      
  
    
  </div>
  


</header>


<section id="preparation" class="level1" data-number="1">
<h1 data-number="1"><span class="header-section-number">1</span> Preparation</h1>
<section id="packages-and-appearance" class="level2" data-number="1.1">
<h2 data-number="1.1" class="anchored" data-anchor-id="packages-and-appearance"><span class="header-section-number">1.1</span> Packages and appearance</h2>
<div class="cell">
<details class="code-fold">
<summary>Show the code</summary>
<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span> (<span class="sc">!</span><span class="fu">require</span>(<span class="st">&quot;pacman&quot;</span>)) {</span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">install.packages</span>(<span class="st">&quot;pacman&quot;</span>)</span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">library</span>(pacman)</span>
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="fu">p_load</span>(here, haven, sandwich, lme4, tibble, patchwork, pandoc, modelsummary, marginaleffects, gt, tidyverse)</span>
<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a><span class="fu">options</span>(</span>
<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a>  <span class="at">ggplot2.continuous.colour =</span> <span class="st">&quot;viridis&quot;</span>,</span>
<span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a>  <span class="at">ggplot2.continuous.fill =</span> <span class="st">&quot;viridis&quot;</span>,</span>
<span id="cb1-11"><a href="#cb1-11" aria-hidden="true" tabindex="-1"></a>  <span class="at">dplyr.summarise.inform =</span> <span class="cn">FALSE</span></span>
<span id="cb1-12"><a href="#cb1-12" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb1-13"><a href="#cb1-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb1-14"><a href="#cb1-14" aria-hidden="true" tabindex="-1"></a>my_theme <span class="ot">&lt;-</span> <span class="cf">function</span>(...) {</span>
<span id="cb1-15"><a href="#cb1-15" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(</span>
<span id="cb1-16"><a href="#cb1-16" aria-hidden="true" tabindex="-1"></a>    <span class="at">plot.title =</span> <span class="fu">element_text</span>(</span>
<span id="cb1-17"><a href="#cb1-17" aria-hidden="true" tabindex="-1"></a>      <span class="at">color =</span> <span class="st">&quot;black&quot;</span>,</span>
<span id="cb1-18"><a href="#cb1-18" aria-hidden="true" tabindex="-1"></a>      <span class="at">size =</span> <span class="dv">12</span>,</span>
<span id="cb1-19"><a href="#cb1-19" aria-hidden="true" tabindex="-1"></a>      <span class="at">face =</span> <span class="st">&quot;bold&quot;</span></span>
<span id="cb1-20"><a href="#cb1-20" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb1-21"><a href="#cb1-21" aria-hidden="true" tabindex="-1"></a>    <span class="at">axis.text =</span> <span class="fu">element_text</span>(</span>
<span id="cb1-22"><a href="#cb1-22" aria-hidden="true" tabindex="-1"></a>      <span class="at">color =</span> <span class="st">&quot;black&quot;</span>,</span>
<span id="cb1-23"><a href="#cb1-23" aria-hidden="true" tabindex="-1"></a>      <span class="at">size =</span> <span class="dv">10</span></span>
<span id="cb1-24"><a href="#cb1-24" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb1-25"><a href="#cb1-25" aria-hidden="true" tabindex="-1"></a>    <span class="at">axis.title =</span> <span class="fu">element_text</span>(</span>
<span id="cb1-26"><a href="#cb1-26" aria-hidden="true" tabindex="-1"></a>      <span class="at">color =</span> <span class="st">&quot;black&quot;</span>,</span>
<span id="cb1-27"><a href="#cb1-27" aria-hidden="true" tabindex="-1"></a>      <span class="at">face =</span> <span class="st">&quot;bold&quot;</span>,</span>
<span id="cb1-28"><a href="#cb1-28" aria-hidden="true" tabindex="-1"></a>      <span class="at">size =</span> <span class="dv">10</span></span>
<span id="cb1-29"><a href="#cb1-29" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb1-30"><a href="#cb1-30" aria-hidden="true" tabindex="-1"></a>    <span class="at">panel.grid.minor =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb1-31"><a href="#cb1-31" aria-hidden="true" tabindex="-1"></a>    <span class="at">strip.background =</span> <span class="fu">element_rect</span>(</span>
<span id="cb1-32"><a href="#cb1-32" aria-hidden="true" tabindex="-1"></a>      <span class="at">fill =</span> <span class="st">&quot;white&quot;</span>,</span>
<span id="cb1-33"><a href="#cb1-33" aria-hidden="true" tabindex="-1"></a>      <span class="at">color =</span> <span class="st">&quot;grey70&quot;</span></span>
<span id="cb1-34"><a href="#cb1-34" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb1-35"><a href="#cb1-35" aria-hidden="true" tabindex="-1"></a>    <span class="at">strip.text =</span> <span class="fu">element_text</span>(<span class="at">color =</span> <span class="st">&quot;black&quot;</span>),</span>
<span id="cb1-36"><a href="#cb1-36" aria-hidden="true" tabindex="-1"></a>    <span class="at">legend.key.size =</span> <span class="fu">unit</span>(<span class="fl">0.3</span>, <span class="st">&quot;cm&quot;</span>)</span>
<span id="cb1-37"><a href="#cb1-37" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb1-38"><a href="#cb1-38" aria-hidden="true" tabindex="-1"></a>}</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
</div>
</section>
<section id="data-and-subsets" class="level2" data-number="1.2">
<h2 data-number="1.2" class="anchored" data-anchor-id="data-and-subsets"><span class="header-section-number">1.2</span> Data and subsets</h2>
<div class="cell">
<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a>df <span class="ot">&lt;-</span> <span class="fu">readRDS</span>(<span class="fu">here</span>(<span class="st">&quot;data&quot;</span>, <span class="st">&quot;ess_short.rds&quot;</span>))</span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a>dfgreen <span class="ot">&lt;-</span> df <span class="sc">%&gt;%</span></span>
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(Green_sample <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> agea_clean <span class="sc">&gt;</span> <span class="dv">24</span>)</span>
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-8"><a href="#cb2-8" aria-hidden="true" tabindex="-1"></a>dfradicalright <span class="ot">&lt;-</span> df <span class="sc">%&gt;%</span></span>
<span id="cb2-9"><a href="#cb2-9" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(RR_sample <span class="sc">==</span> <span class="dv">1</span> <span class="sc">&amp;</span> agea_clean <span class="sc">&gt;</span> <span class="dv">24</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</div>
</section>
</section>
<section id="descriptive-results" class="level1" data-number="2">
<h1 data-number="2"><span class="header-section-number">2</span> Descriptive results</h1>
<section id="party-strength-over-time" class="level2" data-number="2.1">
<h2 data-number="2.1" class="anchored" data-anchor-id="party-strength-over-time"><span class="header-section-number">2.1</span> Party strength over time</h2>
<div class="cell">
<details class="code-fold">
<summary>Show the code</summary>
<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>df <span class="sc">%&gt;%</span></span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(cntry, essround) <span class="sc">%&gt;%</span></span>
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarise</span>(</span>
<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a>    <span class="at">mean_rr_vote =</span> <span class="fu">mean</span>(vote_radright, <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a>    <span class="at">mean_green_vote =</span> <span class="fu">mean</span>(vote_green, <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span>
<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">%&gt;%</span></span>
<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">select</span>(essround, mean_rr_vote, mean_green_vote) <span class="sc">%&gt;%</span></span>
<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a>  <span class="fu">pivot_longer</span>(</span>
<span id="cb3-9"><a href="#cb3-9" aria-hidden="true" tabindex="-1"></a>    <span class="at">cols =</span> <span class="fu">c</span>(mean_rr_vote, mean_green_vote),</span>
<span id="cb3-10"><a href="#cb3-10" aria-hidden="true" tabindex="-1"></a>    <span class="at">names_to =</span> <span class="st">&quot;party&quot;</span>, <span class="at">values_to =</span> <span class="st">&quot;vote&quot;</span></span>
<span id="cb3-11"><a href="#cb3-11" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">%&gt;%</span></span>
<span id="cb3-12"><a href="#cb3-12" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(</span>
<span id="cb3-13"><a href="#cb3-13" aria-hidden="true" tabindex="-1"></a>    <span class="at">party =</span> <span class="fu">case_when</span>(</span>
<span id="cb3-14"><a href="#cb3-14" aria-hidden="true" tabindex="-1"></a>      party <span class="sc">==</span> <span class="st">&quot;mean_rr_vote&quot;</span> <span class="sc">~</span> <span class="st">&quot;Radical right&quot;</span>,</span>
<span id="cb3-15"><a href="#cb3-15" aria-hidden="true" tabindex="-1"></a>      party <span class="sc">==</span> <span class="st">&quot;mean_green_vote&quot;</span> <span class="sc">~</span> <span class="st">&quot;Green&quot;</span></span>
<span id="cb3-16"><a href="#cb3-16" aria-hidden="true" tabindex="-1"></a>    )</span>
<span id="cb3-17"><a href="#cb3-17" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">%&gt;%</span></span>
<span id="cb3-18"><a href="#cb3-18" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>(<span class="fu">aes</span>(</span>
<span id="cb3-19"><a href="#cb3-19" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> essround,</span>
<span id="cb3-20"><a href="#cb3-20" aria-hidden="true" tabindex="-1"></a>    <span class="at">y =</span> vote,</span>
<span id="cb3-21"><a href="#cb3-21" aria-hidden="true" tabindex="-1"></a>    <span class="at">color =</span> party</span>
<span id="cb3-22"><a href="#cb3-22" aria-hidden="true" tabindex="-1"></a>  )) <span class="sc">+</span></span>
<span id="cb3-23"><a href="#cb3-23" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_line</span>(<span class="at">linewidth =</span> .<span class="dv">8</span>) <span class="sc">+</span></span>
<span id="cb3-24"><a href="#cb3-24" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;#35b779&quot;</span>, <span class="st">&quot;#57106e&quot;</span>)) <span class="sc">+</span></span>
<span id="cb3-25"><a href="#cb3-25" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(</span>
<span id="cb3-26"><a href="#cb3-26" aria-hidden="true" tabindex="-1"></a>    <span class="at">labels =</span> scales<span class="sc">::</span>percent,</span>
<span id="cb3-27"><a href="#cb3-27" aria-hidden="true" tabindex="-1"></a>    <span class="at">limits =</span> <span class="fu">c</span>(<span class="dv">0</span>, .<span class="dv">4</span>)</span>
<span id="cb3-28"><a href="#cb3-28" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb3-29"><a href="#cb3-29" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(<span class="at">breaks =</span> <span class="dv">1</span><span class="sc">:</span><span class="dv">10</span>) <span class="sc">+</span></span>
<span id="cb3-30"><a href="#cb3-30" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">color =</span> <span class="cn">NULL</span>, <span class="at">y =</span> <span class="st">&quot;Vote share&quot;</span>, <span class="at">x =</span> <span class="st">&quot;Survey round&quot;</span>) <span class="sc">+</span></span>
<span id="cb3-31"><a href="#cb3-31" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>cntry) <span class="sc">+</span></span>
<span id="cb3-32"><a href="#cb3-32" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_light</span>() <span class="sc">+</span></span>
<span id="cb3-33"><a href="#cb3-33" aria-hidden="true" tabindex="-1"></a>  <span class="fu">my_theme</span>() <span class="sc">+</span></span>
<span id="cb3-34"><a href="#cb3-34" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(</span>
<span id="cb3-35"><a href="#cb3-35" aria-hidden="true" tabindex="-1"></a>    <span class="at">axis.text =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">8</span>),</span>
<span id="cb3-36"><a href="#cb3-36" aria-hidden="true" tabindex="-1"></a>    <span class="at">strip.text =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">8</span>),</span>
<span id="cb3-37"><a href="#cb3-37" aria-hidden="true" tabindex="-1"></a>    <span class="at">legend.position =</span> <span class="st">&quot;top&quot;</span></span>
<span id="cb3-38"><a href="#cb3-38" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb3-39"><a href="#cb3-39" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb3-40"><a href="#cb3-40" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(</span>
<span id="cb3-41"><a href="#cb3-41" aria-hidden="true" tabindex="-1"></a>  <span class="at">filename =</span> <span class="fu">here</span>(<span class="st">&quot;figs&quot;</span>, <span class="st">&quot;final&quot;</span>, <span class="st">&quot;appendix_fig_b1.png&quot;</span>),</span>
<span id="cb3-42"><a href="#cb3-42" aria-hidden="true" tabindex="-1"></a>  <span class="at">units =</span> <span class="fu">c</span>(<span class="st">&quot;in&quot;</span>),</span>
<span id="cb3-43"><a href="#cb3-43" aria-hidden="true" tabindex="-1"></a>  <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">5</span>,</span>
<span id="cb3-44"><a href="#cb3-44" aria-hidden="true" tabindex="-1"></a>  <span class="at">dpi =</span> <span class="dv">500</span></span>
<span id="cb3-45"><a href="#cb3-45" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
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<div id="fig-fig_b1" class="quarto-float quarto-figure quarto-figure-center anchored">
<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-fig_b1-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
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" class="img-fluid figure-img" width="672">
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-fig_b1-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure 1: Vote shares of green and radical right parties by country and ESS round (Appendix)
</figcaption>
</figure>
</div>
</div>
</div>
</section>
<section id="education-by-generation-and-gender" class="level2" data-number="2.2">
<h2 data-number="2.2" class="anchored" data-anchor-id="education-by-generation-and-gender"><span class="header-section-number">2.2</span> Education by generation and gender</h2>
<div class="cell">
<details class="code-fold">
<summary>Show the code</summary>
<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>dfgreen <span class="sc">%&gt;%</span></span>
<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(<span class="sc">!</span><span class="fu">is.na</span>(gndr_fct), <span class="sc">!</span><span class="fu">is.na</span>(edu_fct), <span class="sc">!</span><span class="fu">is.na</span>(cohort_fct)) <span class="sc">%&gt;%</span></span>
<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(cohort_fct, gndr_fct, edu_fct) <span class="sc">%&gt;%</span></span>
<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">count =</span> <span class="fu">n</span>()) <span class="sc">%&gt;%</span></span>
<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">proportion =</span> count <span class="sc">/</span> <span class="fu">sum</span>(count)) <span class="sc">%&gt;%</span></span>
<span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>(<span class="fu">aes</span>(</span>
<span id="cb4-7"><a href="#cb4-7" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> <span class="fu">fct_relevel</span>(</span>
<span id="cb4-8"><a href="#cb4-8" aria-hidden="true" tabindex="-1"></a>      cohort_fct,</span>
<span id="cb4-9"><a href="#cb4-9" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;interwar&quot;</span>, <span class="st">&quot;post-war&quot;</span>,</span>
<span id="cb4-10"><a href="#cb4-10" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;60-70s&quot;</span>, <span class="st">&quot;80s&quot;</span>, <span class="st">&quot;90s&quot;</span>, <span class="st">&quot;millennial&quot;</span></span>
<span id="cb4-11"><a href="#cb4-11" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb4-12"><a href="#cb4-12" aria-hidden="true" tabindex="-1"></a>    <span class="at">y =</span> proportion,</span>
<span id="cb4-13"><a href="#cb4-13" aria-hidden="true" tabindex="-1"></a>    <span class="at">fill =</span> <span class="fu">fct_relevel</span>(</span>
<span id="cb4-14"><a href="#cb4-14" aria-hidden="true" tabindex="-1"></a>      edu_fct,</span>
<span id="cb4-15"><a href="#cb4-15" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;(below) lower secondary&quot;</span>,</span>
<span id="cb4-16"><a href="#cb4-16" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;upper secondary&quot;</span>,</span>
<span id="cb4-17"><a href="#cb4-17" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;tertiary&quot;</span></span>
<span id="cb4-18"><a href="#cb4-18" aria-hidden="true" tabindex="-1"></a>    )</span>
<span id="cb4-19"><a href="#cb4-19" aria-hidden="true" tabindex="-1"></a>  )) <span class="sc">+</span></span>
<span id="cb4-20"><a href="#cb4-20" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">position =</span> <span class="st">&quot;dodge&quot;</span>) <span class="sc">+</span></span>
<span id="cb4-21"><a href="#cb4-21" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_text</span>(<span class="fu">aes</span>(<span class="at">label =</span> scales<span class="sc">::</span><span class="fu">percent</span>(proportion, <span class="at">accuracy =</span> <span class="fl">1.0</span>)),</span>
<span id="cb4-22"><a href="#cb4-22" aria-hidden="true" tabindex="-1"></a>    <span class="at">position =</span> <span class="fu">position_dodge</span>(<span class="at">width =</span> <span class="fl">0.9</span>), <span class="at">vjust =</span> <span class="sc">-</span><span class="fl">0.5</span>, <span class="at">size =</span> <span class="fl">1.8</span></span>
<span id="cb4-23"><a href="#cb4-23" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb4-24"><a href="#cb4-24" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb4-25"><a href="#cb4-25" aria-hidden="true" tabindex="-1"></a>    <span class="at">fill =</span> <span class="cn">NULL</span>, <span class="at">x =</span> <span class="cn">NULL</span>,</span>
<span id="cb4-26"><a href="#cb4-26" aria-hidden="true" tabindex="-1"></a>    <span class="at">y =</span> <span class="st">&quot;Proportion of educational degree&quot;</span></span>
<span id="cb4-27"><a href="#cb4-27" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb4-28"><a href="#cb4-28" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">labels =</span> scales<span class="sc">::</span>percent) <span class="sc">+</span></span>
<span id="cb4-29"><a href="#cb4-29" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_viridis_d</span>(<span class="at">option =</span> <span class="st">&quot;plasma&quot;</span>, <span class="at">end =</span> .<span class="dv">8</span>) <span class="sc">+</span></span>
<span id="cb4-30"><a href="#cb4-30" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>gndr_fct) <span class="sc">+</span></span>
<span id="cb4-31"><a href="#cb4-31" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_light</span>() <span class="sc">+</span></span>
<span id="cb4-32"><a href="#cb4-32" aria-hidden="true" tabindex="-1"></a>  <span class="fu">my_theme</span>() <span class="sc">+</span></span>
<span id="cb4-33"><a href="#cb4-33" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(</span>
<span id="cb4-34"><a href="#cb4-34" aria-hidden="true" tabindex="-1"></a>    <span class="at">legend.position =</span> <span class="st">&quot;top&quot;</span>,</span>
<span id="cb4-35"><a href="#cb4-35" aria-hidden="true" tabindex="-1"></a>    <span class="at">axis.text.x =</span> <span class="fu">element_text</span>(</span>
<span id="cb4-36"><a href="#cb4-36" aria-hidden="true" tabindex="-1"></a>      <span class="at">angle =</span> <span class="dv">90</span>,</span>
<span id="cb4-37"><a href="#cb4-37" aria-hidden="true" tabindex="-1"></a>      <span class="at">vjust =</span> <span class="fl">0.5</span>,</span>
<span id="cb4-38"><a href="#cb4-38" aria-hidden="true" tabindex="-1"></a>      <span class="at">hjust =</span> <span class="dv">1</span></span>
<span id="cb4-39"><a href="#cb4-39" aria-hidden="true" tabindex="-1"></a>    )</span>
<span id="cb4-40"><a href="#cb4-40" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb4-41"><a href="#cb4-41" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb4-42"><a href="#cb4-42" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(</span>
<span id="cb4-43"><a href="#cb4-43" aria-hidden="true" tabindex="-1"></a>  <span class="at">filename =</span> <span class="fu">here</span>(<span class="st">&quot;figs&quot;</span>, <span class="st">&quot;final&quot;</span>, <span class="st">&quot;appendix_fig_b2.png&quot;</span>),</span>
<span id="cb4-44"><a href="#cb4-44" aria-hidden="true" tabindex="-1"></a>  <span class="at">units =</span> <span class="fu">c</span>(<span class="st">&quot;in&quot;</span>),</span>
<span id="cb4-45"><a href="#cb4-45" aria-hidden="true" tabindex="-1"></a>  <span class="at">width =</span> <span class="dv">6</span>, <span class="at">height =</span> <span class="dv">4</span>,</span>
<span id="cb4-46"><a href="#cb4-46" aria-hidden="true" tabindex="-1"></a>  <span class="at">dpi =</span> <span class="dv">300</span></span>
<span id="cb4-47"><a href="#cb4-47" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<div id="fig-fig_b2" class="quarto-float quarto-figure quarto-figure-center anchored">
<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-fig_b2-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<img role="img" src="data:image/png;base64,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" class="img-fluid figure-img" width="672">
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-fig_b2-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure 2: Education by cohort and gender (Appendix)
</figcaption>
</figure>
</div>
</div>
</div>
</section>
<section id="green-vote-by-generation-education-and-gender" class="level2" data-number="2.3">
<h2 data-number="2.3" class="anchored" data-anchor-id="green-vote-by-generation-education-and-gender"><span class="header-section-number">2.3</span> Green vote by generation, education and gender</h2>
<div class="cell">
<details class="code-fold">
<summary>Show the code</summary>
<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>dfgreen <span class="sc">%&gt;%</span></span>
<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(<span class="sc">!</span><span class="fu">is.na</span>(gndr_fct), <span class="sc">!</span><span class="fu">is.na</span>(edu_fct), <span class="sc">!</span><span class="fu">is.na</span>(cohort_fct)) <span class="sc">%&gt;%</span></span>
<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(edu_fct, cohort_fct, gndr_fct) <span class="sc">%&gt;%</span></span>
<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">mvotegreen =</span> <span class="fu">mean</span>(vote_green, <span class="at">na.rm =</span> <span class="cn">TRUE</span>)) <span class="sc">%&gt;%</span></span>
<span id="cb5-5"><a href="#cb5-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>(<span class="fu">aes</span>(</span>
<span id="cb5-6"><a href="#cb5-6" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> <span class="fu">fct_relevel</span>(</span>
<span id="cb5-7"><a href="#cb5-7" aria-hidden="true" tabindex="-1"></a>      cohort_fct,</span>
<span id="cb5-8"><a href="#cb5-8" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;interwar&quot;</span>, <span class="st">&quot;post-war&quot;</span>, <span class="st">&quot;60-70s&quot;</span>,</span>
<span id="cb5-9"><a href="#cb5-9" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;80s&quot;</span>, <span class="st">&quot;90s&quot;</span>, <span class="st">&quot;millennial&quot;</span></span>
<span id="cb5-10"><a href="#cb5-10" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb5-11"><a href="#cb5-11" aria-hidden="true" tabindex="-1"></a>    <span class="at">y =</span> mvotegreen,</span>
<span id="cb5-12"><a href="#cb5-12" aria-hidden="true" tabindex="-1"></a>    <span class="at">fill =</span> <span class="fu">fct_relevel</span>(</span>
<span id="cb5-13"><a href="#cb5-13" aria-hidden="true" tabindex="-1"></a>      edu_fct,</span>
<span id="cb5-14"><a href="#cb5-14" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;(below) lower secondary&quot;</span>,</span>
<span id="cb5-15"><a href="#cb5-15" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;upper secondary&quot;</span>,</span>
<span id="cb5-16"><a href="#cb5-16" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;tertiary&quot;</span></span>
<span id="cb5-17"><a href="#cb5-17" aria-hidden="true" tabindex="-1"></a>    )</span>
<span id="cb5-18"><a href="#cb5-18" aria-hidden="true" tabindex="-1"></a>  )) <span class="sc">+</span></span>
<span id="cb5-19"><a href="#cb5-19" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">position =</span> <span class="st">&quot;dodge&quot;</span>) <span class="sc">+</span></span>
<span id="cb5-20"><a href="#cb5-20" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_text</span>(<span class="fu">aes</span>(<span class="at">label =</span> scales<span class="sc">::</span><span class="fu">percent</span>(mvotegreen, <span class="at">accuracy =</span> <span class="fl">1.0</span>)),</span>
<span id="cb5-21"><a href="#cb5-21" aria-hidden="true" tabindex="-1"></a>    <span class="at">position =</span> <span class="fu">position_dodge</span>(<span class="at">width =</span> <span class="fl">0.9</span>), <span class="at">vjust =</span> <span class="sc">-</span><span class="fl">0.5</span>, <span class="at">size =</span> <span class="dv">2</span></span>
<span id="cb5-22"><a href="#cb5-22" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb5-23"><a href="#cb5-23" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb5-24"><a href="#cb5-24" aria-hidden="true" tabindex="-1"></a>    <span class="at">fill =</span> <span class="cn">NULL</span>, <span class="at">x =</span> <span class="cn">NULL</span>,</span>
<span id="cb5-25"><a href="#cb5-25" aria-hidden="true" tabindex="-1"></a>    <span class="at">y =</span> <span class="st">&quot;Vote share greens&quot;</span></span>
<span id="cb5-26"><a href="#cb5-26" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb5-27"><a href="#cb5-27" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">labels =</span> scales<span class="sc">::</span>percent) <span class="sc">+</span></span>
<span id="cb5-28"><a href="#cb5-28" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_viridis_d</span>(<span class="at">option =</span> <span class="st">&quot;plasma&quot;</span>, <span class="at">end =</span> .<span class="dv">8</span>) <span class="sc">+</span></span>
<span id="cb5-29"><a href="#cb5-29" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>gndr_fct) <span class="sc">+</span></span>
<span id="cb5-30"><a href="#cb5-30" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_light</span>() <span class="sc">+</span></span>
<span id="cb5-31"><a href="#cb5-31" aria-hidden="true" tabindex="-1"></a>  <span class="fu">my_theme</span>() <span class="sc">+</span></span>
<span id="cb5-32"><a href="#cb5-32" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(</span>
<span id="cb5-33"><a href="#cb5-33" aria-hidden="true" tabindex="-1"></a>    <span class="at">legend.position =</span> <span class="st">&quot;top&quot;</span>,</span>
<span id="cb5-34"><a href="#cb5-34" aria-hidden="true" tabindex="-1"></a>    <span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle =</span> <span class="dv">90</span>, <span class="at">vjust =</span> <span class="fl">0.5</span>, <span class="at">hjust =</span> <span class="dv">1</span>)</span>
<span id="cb5-35"><a href="#cb5-35" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb5-36"><a href="#cb5-36" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb5-37"><a href="#cb5-37" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(</span>
<span id="cb5-38"><a href="#cb5-38" aria-hidden="true" tabindex="-1"></a>  <span class="at">filename =</span> <span class="fu">here</span>(<span class="st">&quot;figs&quot;</span>, <span class="st">&quot;final&quot;</span>, <span class="st">&quot;appendix_fig_b3.png&quot;</span>),</span>
<span id="cb5-39"><a href="#cb5-39" aria-hidden="true" tabindex="-1"></a>  <span class="at">units =</span> <span class="fu">c</span>(<span class="st">&quot;in&quot;</span>),</span>
<span id="cb5-40"><a href="#cb5-40" aria-hidden="true" tabindex="-1"></a>  <span class="at">width =</span> <span class="dv">6</span>, <span class="at">height =</span> <span class="dv">4</span>,</span>
<span id="cb5-41"><a href="#cb5-41" aria-hidden="true" tabindex="-1"></a>  <span class="at">dpi =</span> <span class="dv">300</span></span>
<span id="cb5-42"><a href="#cb5-42" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<div id="fig-fig_b3" class="quarto-float quarto-figure quarto-figure-center anchored">
<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-fig_b3-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
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" class="img-fluid figure-img" width="672">
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-fig_b3-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure 3: Voting for a green party by cohort, education, and gender (Appendix)
</figcaption>
</figure>
</div>
</div>
</div>
</section>
<section id="radical-right-vote-by-generation-education-and-gender" class="level2" data-number="2.4">
<h2 data-number="2.4" class="anchored" data-anchor-id="radical-right-vote-by-generation-education-and-gender"><span class="header-section-number">2.4</span> Radical right vote by generation, education and gender</h2>
<div class="cell">
<details class="code-fold">
<summary>Show the code</summary>
<div class="sourceCode cell-code" id="cb6"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>dfradicalright <span class="sc">%&gt;%</span></span>
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">filter</span>(<span class="sc">!</span><span class="fu">is.na</span>(gndr_fct), <span class="sc">!</span><span class="fu">is.na</span>(edu_fct), <span class="sc">!</span><span class="fu">is.na</span>(cohort_fct)) <span class="sc">%&gt;%</span></span>
<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(edu_fct, cohort_fct, gndr_fct) <span class="sc">%&gt;%</span></span>
<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">mvoterr =</span> <span class="fu">mean</span>(vote_radright, <span class="at">na.rm =</span> <span class="cn">TRUE</span>)) <span class="sc">%&gt;%</span></span>
<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>(<span class="fu">aes</span>(</span>
<span id="cb6-6"><a href="#cb6-6" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> <span class="fu">fct_relevel</span>(</span>
<span id="cb6-7"><a href="#cb6-7" aria-hidden="true" tabindex="-1"></a>      cohort_fct,</span>
<span id="cb6-8"><a href="#cb6-8" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;interwar&quot;</span>, <span class="st">&quot;post-war&quot;</span>, <span class="st">&quot;60-70s&quot;</span>, <span class="st">&quot;80s&quot;</span>, <span class="st">&quot;90s&quot;</span>, <span class="st">&quot;millennial&quot;</span></span>
<span id="cb6-9"><a href="#cb6-9" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb6-10"><a href="#cb6-10" aria-hidden="true" tabindex="-1"></a>    <span class="at">y =</span> mvoterr,</span>
<span id="cb6-11"><a href="#cb6-11" aria-hidden="true" tabindex="-1"></a>    <span class="at">fill =</span> <span class="fu">fct_relevel</span>(</span>
<span id="cb6-12"><a href="#cb6-12" aria-hidden="true" tabindex="-1"></a>      edu_fct,</span>
<span id="cb6-13"><a href="#cb6-13" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;(below) lower secondary&quot;</span>,</span>
<span id="cb6-14"><a href="#cb6-14" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;upper secondary&quot;</span>,</span>
<span id="cb6-15"><a href="#cb6-15" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;tertiary&quot;</span></span>
<span id="cb6-16"><a href="#cb6-16" aria-hidden="true" tabindex="-1"></a>    )</span>
<span id="cb6-17"><a href="#cb6-17" aria-hidden="true" tabindex="-1"></a>  )) <span class="sc">+</span></span>
<span id="cb6-18"><a href="#cb6-18" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>(<span class="at">position =</span> <span class="st">&quot;dodge&quot;</span>) <span class="sc">+</span></span>
<span id="cb6-19"><a href="#cb6-19" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_text</span>(<span class="fu">aes</span>(<span class="at">label =</span> scales<span class="sc">::</span><span class="fu">percent</span>(mvoterr, <span class="at">accuracy =</span> <span class="fl">1.0</span>)),</span>
<span id="cb6-20"><a href="#cb6-20" aria-hidden="true" tabindex="-1"></a>    <span class="at">position =</span> <span class="fu">position_dodge</span>(<span class="at">width =</span> <span class="fl">0.9</span>), <span class="at">vjust =</span> <span class="sc">-</span><span class="fl">0.5</span>, <span class="at">size =</span> <span class="dv">2</span></span>
<span id="cb6-21"><a href="#cb6-21" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb6-22"><a href="#cb6-22" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb6-23"><a href="#cb6-23" aria-hidden="true" tabindex="-1"></a>    <span class="at">fill =</span> <span class="cn">NULL</span>, <span class="at">x =</span> <span class="cn">NULL</span>,</span>
<span id="cb6-24"><a href="#cb6-24" aria-hidden="true" tabindex="-1"></a>    <span class="at">y =</span> <span class="st">&quot;Vote share radical right&quot;</span></span>
<span id="cb6-25"><a href="#cb6-25" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb6-26"><a href="#cb6-26" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(<span class="at">labels =</span> scales<span class="sc">::</span>percent) <span class="sc">+</span></span>
<span id="cb6-27"><a href="#cb6-27" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_fill_viridis_d</span>(<span class="at">option =</span> <span class="st">&quot;plasma&quot;</span>, <span class="at">end =</span> .<span class="dv">8</span>) <span class="sc">+</span></span>
<span id="cb6-28"><a href="#cb6-28" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>gndr_fct) <span class="sc">+</span></span>
<span id="cb6-29"><a href="#cb6-29" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_light</span>() <span class="sc">+</span></span>
<span id="cb6-30"><a href="#cb6-30" aria-hidden="true" tabindex="-1"></a>  <span class="fu">my_theme</span>() <span class="sc">+</span></span>
<span id="cb6-31"><a href="#cb6-31" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(</span>
<span id="cb6-32"><a href="#cb6-32" aria-hidden="true" tabindex="-1"></a>    <span class="at">legend.position =</span> <span class="st">&quot;top&quot;</span>,</span>
<span id="cb6-33"><a href="#cb6-33" aria-hidden="true" tabindex="-1"></a>    <span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle =</span> <span class="dv">90</span>, <span class="at">vjust =</span> <span class="fl">0.5</span>, <span class="at">hjust =</span> <span class="dv">1</span>)</span>
<span id="cb6-34"><a href="#cb6-34" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb6-35"><a href="#cb6-35" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb6-36"><a href="#cb6-36" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(</span>
<span id="cb6-37"><a href="#cb6-37" aria-hidden="true" tabindex="-1"></a>  <span class="at">filename =</span> <span class="fu">here</span>(<span class="st">&quot;figs&quot;</span>, <span class="st">&quot;final&quot;</span>, <span class="st">&quot;appendix_fig_b4.png&quot;</span>),</span>
<span id="cb6-38"><a href="#cb6-38" aria-hidden="true" tabindex="-1"></a>  <span class="at">units =</span> <span class="fu">c</span>(<span class="st">&quot;in&quot;</span>),</span>
<span id="cb6-39"><a href="#cb6-39" aria-hidden="true" tabindex="-1"></a>  <span class="at">width =</span> <span class="dv">6</span>, <span class="at">height =</span> <span class="dv">4</span>,</span>
<span id="cb6-40"><a href="#cb6-40" aria-hidden="true" tabindex="-1"></a>  <span class="at">dpi =</span> <span class="dv">300</span></span>
<span id="cb6-41"><a href="#cb6-41" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<div id="fig-fig_b4" class="quarto-float quarto-figure quarto-figure-center anchored">
<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-fig_b4-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<img role="img" 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IACGMAXQIDKSO9xm4wBAQpgAF8AASojvcdtMgYEKIABfAEEqIz0Hjd0g5tPX8R/bi6i6OjSmFUUncbfro69tU+AhjSAL4AAlZHe44ZusJi9sPyM6bk4urw5O1+/98Lc/OK1t/YJ0JAG8AUQoDLSe9ywDW7OIgtQS037x/r915uLc7M89dW+FQEazgC+AAJURnqPG7bB6njLzhxACzugBCiCAXwBBKiM9B43dAPHzv0hfGEHlABFMIAvgACVkd7jhm7gAGrMYncTKe6ALtzA3kv7BGhIA/gCCFAZ6T1u6AYOoDdnx3Ev9E4ycl+exiP5lcWpj/YJ0JAG8AUQoDLSe9zQDRxAV5adaV807oAujy7Tb/q3T4CGNIAvgACVkd7jhm5wANDFuSFAUQ3gCyBAZaT3uKEbZEN4exPJfv/BpeEQHtUAvgACVEZ6jxu6QdLVvDlLbiIZOw3UmCVvImEawBdAgMpI73GbjAEBCmAAXwABKiO9x20yBgQogAF8AQSojPQet8kYEKAABvAFEKAy0nvcJmNAgAIYwBdAgMpI73GbjAEBCmAAXwABKiO9xw3b4F9XyUP7VgRoOAP4AghQGek9btgGBOi4DOALIEBlpPe4YRsQoOMygC+AAJWR3uOGbUCAjssAvgACVEZ6jxu2AQE6LgP4AghQGek9btgGBOi4DOALIEBlpPe4YRsQoOMygC+AAJWR3uOGbUCAjssAvgACVEZ6jxu2AQE6LgP4AghQGek9btgGBOi4DOALIEBlpPe4YRsQoOMygC+AAJWR3uOGbUCAjssAvgACVEZ6jxu2AQE6LgP4AghQGek9btgGBOi4DOALIEBlpPe4YRsQoOMygC+AAJWR3uOGbUCAjssAvgACVEZ6jxu2AQE6LgP4AghQGek9btgGBOi4DOALIEBlpPe4YRsQoOMygC+AAJWR3uOGbUCAjssAvgACVEZ6jxu2AQE6LgP4AghQGek9btgGBOi4DOALIEBlpPe4YRsQoOMygC+AAJWR3uOGbUCAjssAvgACVEZ6jxu2AQE6LgP4AghQGek9btgGBOi4DOALIEBlpPe4YRsQoOMyUFvAzacvjFlFTuf2i9P4L1fH/gwI0EHbn6oBATouA7UFLGYvtl8dXd6cna/fe2FufvHanwEBOmj7UzUgQMdloLSAm7NoC9BV/MX6/debi3OzPPVmYAjQYdufqgEBOi4DpQWsjm2P02pzEQ/bE4AWdkAJUBkpDQa8AQE6LgO1BWwBajugJhnCF3ZACVAZqQ0GuAEBOi4DtQVsAbpw943sTaS4A7rIBvb9DQjQQdufqgEBOi4DtQWkAI37ntu/WZ7GI/nV0aUnAwJ00PanakCAjstAbQEpQFd3ttc94w7o8uhy2zHtb0CADtr+VA0I0HEZqC0gJeUy63Euzg0ButNkgwFuQICOy0BtASkpF9up8+sPLg2H8DtNNhjgBgTouAzUFpAAdHOR3nm300Dj/ihvIm012WCAGxCg4zKAL4AAlZHe44ZtQICOywC+AAJURnqPG7YBATouA/gCCFAZ6T1u2AYE6LgM4AsgQGWk97hhGxCg4zKAL4AAlZHe44ZtQICOy0BnAS1SRoDKSGcw8A0I0HEZ6CyAAK3VNIOBb0CAjstAZwEEaK2mGQx8AwJ0XAY6CyBAazXNYOAbEKDjMtBZAAFaq2kGA9+AAB2Xgc4CCNBaTTMY+AYE6LgMdBZAgNZqmsHANyBAx2WgswACtFbTDAa+AQE6LgOdBRCgtZpmMPANCNBxGegsgACt1TSDgW9AgI7LQGcBBGitphkMfAMCdFwGOgsgQGs1zWDgGxCg4zLQWQABWqtpBgPfgAAdl4HOAgjQWk0zGPgGBOi4DHQWQIDWaprBwDcgQMdloLMAArRW0wwGvgEBOi4DnQUQoLWaZjDwDQjQcRnoLIAArdU0g4FvQICOy0BnAQRoraYZDHwDAnRcBjoLIEBrNc1g4BsQoOMy0FkAAVqraQYD34AAHZeBzgII0FpNMxj4BgTouAx0FkCA1mqawcA3IEDHZaCzAAK0VtMMBr4BATouA50FEKC1mmYw8A0I0HEZ6CyAAK3VNIOBb0CAjstAZwEEaK2mGQx8AwJ0XAY6CyBAazXNYOAbEKDjMtBZAAFaq2kGA9+AAB2Xgc4CCNBaTTMY+AYE6LgMdBZAgNZqmsHANyBAx2WgswACtFbTDAa+AQE6LgOdBRCgtZpmMPANCNBxGegsgACt1TSDgW9AgI7LQGcBBGitphkMfAMCdFwGOgsgQGs1zWDgGxCg4zLQWQABWqtpBgPfoCbaN5++iD+0nkdRdPQvZhVFp/G3q+N2HgRoOAOdBRCgtZpmMPANaqK9mFmAro4ubfs3Z+fr916Ym1+8budBgIYz0FkAAVqraQYD36Ay2jdnkQPo0nU5r9bvv95cnJvlaUsPAjScgc4CCNBaTTMY+AaV0V4d2x5n3A91yEwB2roDSoAGNNBZAAFaq2kGI4RB/jLkZdVlSIloO4DenJ1E0Z3X6RC+dQeUAA1ooLMAArRW0wxGCIP8ZUhTdRlSDKDreczM9QfJTaTYepEM7BuLAA1noLMAArRW0wyGvMH+ZUhTdRlSDKDphnzi/rs8jbchxXlDEaDhDHQWQIDWaprBkDfYvwxpqi5DBgFobL08utz9dRMRoOEMdBZAgNZqmsEIYZC/DGmqLkOKAXQVG5ubT39tP7+Iu78EqF4DnQUQoLWaZjBCGOQvQ15WXYYUA6i9aGBWR/9ikk3gEF6xgc4CCNBaTTMYIQzyo+hz99+Sy5ByQ/ibMzsD4CpFqVnyJpJaA50FEKC1mmYwQhgcALTsMqR0tL0lW2/K8A10FkCA1mqawQhhkL8M6ZhZdhmSAO0veAOdBRCgtZpmMEIY5C9D2lF76WVIArS/4A10FkCA1mqawQhhkL8MaaouQxKg/QVvoLMAArRW0wwGvgEBOi4DnQUQoLWaZjDwDSqj/R+r1NyDAA1noLMAArRW0wwGvgEBOi4DnQUQoLWaZjDwDQjQcRnoLIAArdU0g4FvQICOy0BnAQRoraYZDHED8eQRoOMy0FkAAVqraQZD3IAA3ZPOg6TJQGcBBGitphkMcQMCdE86D5Img67tD/reg/4GVgTokO0rNSBA96TzIGky6Nr+oO896G9g5Qmgm4s727KXUarkF8mb+NfLw/T5v5uzwgV19EYbPtlKk0eAjsugW/sDv/egv4GVJ4Auowygiz2AJjhN/3EZFfTONUcbPtlKk0eAjsugW/sDv/egv4GVH4AuM0be6mXGv2Q+vrw+c9c2yjqgiqMNn2ylySNAx2XQtf1B33vQ38DKB0A3X+06mWY9zy/Ys3J/v0xWpCjpgCqONnyylSaPAB2XQS+ADvXeg/4GVh4A+vZRFJ1kAF3tUTJBZ4LR9fxOUe9cc7Thk600eQTouAx6AdRpgPce9Dew6g9Qeyn42SoD6DI6z/1jHqCLqLB3rjna8MlWmjwCdFwG3gA61HsPhgXoR5dmB9DFnf/wfB5FD7513+WG8KUdUMXRhk+20uQRoOMy6AVQDe89GHQI/6MxO4DaJXgTpfeNsptIpR1QxdFWm2zwGcgE6LgMegFUw3sPhr4LvwNofE7Pnl6ad1+lBE2mMd17XdEBVRxttckGn4FMgI7LoN8QXsF7D/QANO4MJddAF+nfvLkfRfEYv6gD+oetrqhW+uvPo7/7dfzff/5f3bc//Zvf/+1Xn1z98898tV+ZPHGDSoA29zgAqI8Np4AkHmMr3wDNdHO2R8yVuwz6KO6Mvjr4Yb19A6VdA/gZyN0B2tyDPdBwBjoLEI+xlRhANxf5+Uzxd+fJJbtodm5uSW+0dQbDwM9AJkDHZaCzAGyAxmP24/y/xx3QRfzHZhEdXCHWG22dwTDwM5AJ0HEZ6CxgRAB1HdB4UH9uz/qDs1xvtHUGw8DPQCZAx2WgswBsgMbMPM39s5sIOnPjzuj2GF5vtHUGw8DPQCZAx2XQrf0WMdMZYyvfAM1G6Ov5DpSuA0qAejQAn4FMgI7LgADN1H8aUzJCj5m5G04mz8dzCO/PAHwGMgE6LgMCNFNfgMbgnD0z5vpRtOtopsvYOabyJpIXA/AZyATouAwI0Ey9r4FadLrZSs+yf9wuY5dOYzo4yfVGGz7ZSpNHgI7LgADN1P8m0ubl/ZiSD3fz5XfrKF9zIj2EQV3y0uuuriN8VfUkficDAhTOgADNxJfKhWxfqUFd8hbpb8TNxezFVdWT+J0MCFA4AwI0EwEasn2lBtXJ274LzJjl3bgHWvUusE4GBCicAQGaiQAN2b5Sg+rkbZ/EN+v3/3MG0FYdUAJ0ZAYEaCYCNGT7SoNRZ5AANMamvQZa9SR+JwMCFM6AAM1EgIZsX2kw6gwSgC6PdzeRSp7E72RAgMIZEKCZCNCQ7SsNRp1BspTJ+68TgFqVPInfyYAAhTMgQDMRoCHbVxqMOgMH0ORdA9En9gfKnsTvZECAwhkQoJkI0JDtKw1GnUFGym0PtOxJ/E4GBCicAQGaiQAN2b7SYNQZ3AZo6ZP4nQwIUDgDAjQTARqyfaXBqDO4BdDyJ/E7GRCgcAYEaCYCNGT7SoMxrAEBCmdAgGYiQEO2rzQYwxoQoHAGBGgmAjRk+0qDMawBAQpnQIBmIkBDtq80GMMaEKBwBgRoJgI0ZPtKgzGsAQEKZ0CAZiJAQ7avNBjifBM3MARoSAMCNBMBGrJ9pcEQ55u4gSFAQxoQoJkI0JDtKw2GON/EDQwBGtKAAM1EgIZsX2kwxPkmbmAI0JAGBGgmAjRk+0qDIc43cQNDgIY0IEAzEaAh21caDHG+iRsYAjSkAQGaiQAN2b7SYIjzTdzAEKAhDQjQTARoyPaVBkOcb+IGhgANaUCAZiJAQ7avNBjifBM3MARoSAMCNBMBGrJ9pcEQ55u4gSFAQxoQoJkI0JDtKw2GON/EDQwBGtKAAM1EgIZsX2kwxPkmbmAI0JAGBGgmAjRk+0qDIc43cQNDgIY0IEAzEaAh21caDHG+iRsYAjSkAQGaiQAN2b7SYIjzTdzAEKAhDQjQTARoyPaVBkOcb+IGhgANaUCAZiJAQ7avNBjifBM3MARoSAMCNFMhQDe/efL59pXf68f3P+pqVrYNnX9SZzCkDcSDIc43cQNDgIY0IEAzFQL05iy683r39dFl0Ye6S2+0dSZbPBjifBM3MARoSAMCNFMtQNfz3deepDfaOpMtHgxxvokbGAI0pAEBmukAoIvoQATosAbiwRDnm7iBIUBDGhCgmQ4AGvc4b4tD+GENxIMhzjdxA0OAhjQgQDMdDuEPu6DnXc3KtqHzT+oMhrSBeDDE+SZuYAjQkAYEaKZDgK4fn9yPqXn3JNGDz1919Srdhs4/qTMY0gbiwRDnm7iBIUBDGhCgmWpvIglIb7R1Jls8GOJ8EzcwBGhIAwI0U+08UAHpjbbOZIsHQ5xv4gaGAA1pQIBm4pNIIdtXGgxxvokbGAI0pAEBmokADdm+0mCI803cwBCgIQ0I0EylAP3+m29++ySR7+G83mjrTLZ4MMT5Jm5gCNCQBgRophKAvsnPBuVE+mENxIMhzjdxA0OAhjQgQDMVA3TFJ5EUGYgHQ5xv4gaGAA1pQIBmKr4Lf0GAKjIQD4Y438QNDAEa0oAAzVQ2DzS697tvf0j1Y1ezsm3o/JM6gyFtIB4Mcb6JGxgCNKQBAZqpDKCzF10dGmxD55/UGQxpA/FgiPNN3MAQoCENCNBMZUN4Pomkx0A8GOJ8EzcwBGhIAwI0U9lNJPZA9RiIB0Ocb+IGhgANaUCAZiqZxrSI7nhfQ2S3DZ1/UmcwpA3EgyHON3EDQ4CGNCBAM90G6M1jtwaTXY9pdrJdkIl34Qc1EA+GON/EDQwBGtKAAM10ANAzrkivzUA8GOJ8EzcwBGhIAwI0EwEasn2lwRDnm7iBIUBDGhCgmQjQkO0rDYY438QNDAEa0oAAzcTVmEK2rzQY4nwTNzAEaEgDAjQTARqyfaXBEOebuIEhQEMaEKCZCNCQ7csE4+ZTO2nXXny58/rKLgRzGn+7OvZkQIB6F7wBAZqJAA3ZvkwwFvaph5uzmJiLo3+5OTtfv/fC3PyixYVrcb6JGxgCNKQBAZqp+Fn4xyd5ffj5n7vaFW9D55/UGQxpg+pgpAsXrOwf6/d+vX7/9ebi3CxPfRkQoN4Fb0CAZipdjWlf93w+2ak32jqTXR2M1bHtcSbKANqqA0qA7ktnCjQZEKCZGgLU61QmvdHWmey6YOwAmg3hW3VACdB96UyBJgMCNFPZEP6AoK3OyJpt6PyTOoMhbVAXjAygy9mL5CZS3AFdtFgPRpxv4gaGAA1pQIBmKrmJtIyij+w6yu++imbnZrOIoiN/b5bTG22dya4Lxhagy+g8NViexiP5VeNDJs43cQNDgIY0IEAzlb4TadvjXNqezHrucwyvN9o6k10XjBSg7ma8M4g7oMujy93IvpcBAepd8AYEaKbaBaoGkiQAACAASURBVJVjdh7bi6IE6GAGdcFISLl0R8gZLM4NAao3ZfgGBGimsptIGS/d1wTokAZ1wXCkTHFpDdYfXBoO4fWmDN+AAM1Uehf+PP16FRGgAxvUBcOxc5nc6/vEjh/ssVvyJlJX6UyBJgMCNFPpa41nz9yXb+b2/lEMUN5EGsxAPBjifBM3MARoSAMCNFPxTaSkO3NycpLOYIq7oS0era7dhs4/qTMY0gbiwRDnm7iBIUBDGhCgmYoBarugmeK+53I3pPcgvdHWmWzxYIjzTdzAEKAhDQjQTCXzQDfPM34+vLQ8/airYdE2dP5JncGQNhAPhjjfxA0MARrSgADNVLoa0/fP7Yvl7j60L+fc/PHbrn6F29D5J3UGQ9pAPBjifBM3MARoSAMCNBOXswvZ/lT5Jm5gCNCQBgRoJgI0ZPsEqJCBIUBDGhCgmQhQH+3n14Q3VWvCE6AyBoYADWlAgGY6eCvn45MHr28tqPyAb+WsUX5N+MuqNeEJUBkDQ4CGNCBAMxW81tg9eMTXGjfX/prwL6rWhCdAZQwMARrSgADNRID2b39/TfgXVWvCE6AyBoYADWlAgGYiQH20n18T/rJqTXgCVMbAEKAhDQjQTLyJ5KP9/JrwpmpNeAJUxsAQoCENCNBMBKiP9vNrwicqWROeAJUxMARoSAMCNFMxQP/y1N/aSwXb0PkndQZjf014p7I14QlQGQNDgIY0IEAzlS1n53H1usNt6PyTOoOxvya8U9ma8ASojIEhQEMaEKCZalekF5DeaPcCaA6XpWvCE6AyBoYADWlAgGYq64E2X868wzZ0/kmdwdhfE/68ak14AlTGwBCgIQ0I0Eylb+W896qrRf02dP5JncGQNsDnm7iBIUBDGhCgmYp7oN+55UDv8lFOFQb4fBM3MARoSAMCNFPpS+U4kV6NAT7fxA0MARrSgADNRICGbJ8AFTIwBGhIAwI0EwEasn0CVMjAEKAhDQjQTHwSqX/7gwYDgm/iBoYADWkAeJ54qcAQoBLtDxoMCL6JGxgCNKQB4HnipQJDgEq0P2gwIPgmbmAI0JAGgOeJlwoMASrR/qDBgOCbuIEhQEMaAJ4nXiowBKhE+4MGA4Jv4gaGAA1pAHieeKnAEKAS7Q8aDAi+iRsYAjSkAeB54qUCQ4BKtD9oMCD4Jm5gCNCQBoDniZcKDAEq0f6gwYDgm7iBIUBDGgCeJ14qMASoRPuDBgOCb+IGhgANaQB4nnipwBCgEu0PGgwIvokbGAI0pAHgeeKlAkOASrQ/aDAg+CZuYAjQkAaA50n87zefJuvz2v9euVc/GvtO8nYmBKj/9gcNBgTfxA0MARrSAPA8MbtXltn/XiUvH7cvL2tnUvte+McnB+J6oNUaNBgQfBM3MARoSAPA88SukOQAmvz3av3+a/viiOVpS5NagO4vxMTVmBpo0GBA8E3cwBCgIQ0Az5N4rJ68tCz5bwrQ1h1QAlSg/UGDAcE3cQNDgIY0ADxPzO6tjw6gyRC+dQeUABVof9BgQPBN3MAQoCENAM8Tcwug7iZS3AFdtHx/Jm8i+W9/0GBA8E3cwBCgHgzyt6lN1W1qwPPE3Aao1fK08OXjVSJA/bc/aDAg+CZuYAhQDwb529Sm6jY14HliCgAa17Y8utz+dTMRoP7bHzQYEHwTNzAEaG+D/dvUpuo2NeB5YgoAuojLkwfo5u0veQ20UoMGA4Jv4gaGAO1tsH+b2lTdpgY8T8whQNcfXBqpIfz1Y95EaqxBgwHBN3EDQ4B6MMgTxlTdpgY8T8wBQG3/2pilyE2k/XvxBGi1Bg0GBN/EDQwB6sFgD6BVt6kBzxMvFZimAF1yGlMLDRoMCL6JGxgC1IPBPkCtSm5TA54nXiowDQG6uYixefd+/P+T+ItnXb1Kt6HzTxKgIvjBNzAEqAeDA4CW3aYGPE+8VGAaAtSO4J8lsxlilvrugCqONmAwIPgmbmAIUA8GBwAtu00NeJ54qcA0B6il5tJOpF3Po5YLPtVvQ+efJEBF8INvYAhQDwa3AVp6mxrwPPFSgWkH0JVFZ/x1q9v8Tbah808SoCL4wTcwBKgHg1sALb9NPdHzxKrxNVDX+YwxuuJNpDrBBwPfwBCgIQ0mep5YNb8Lf+d1zNG7n80J0DrBBwPfwBCgIQ0mep5YNZ8HGmMznc3Ea6DVgg8GvoEhQEMaTPQ8sWr4JNLmuQVoMp++3Uz9JtvQ+ScZDJFg4BsYAjSkwUTPE6vGz8K/+6dLY64fRdE93/xUHO2JBgPfwBCgIQ0mep5YtV5MxPMdeLcNnX8SMhjpKouLKDq67PQyQPFg4BsYArS3QU2Me7c/UYAKSG+0RYKRrLK4PLrcXBx3ehmgeDDwDQwB2tugJsa9258WQN+cpFPC5h9574PqjbZAMNLVFWNwxt3OO7/v8jJA8WDgGxgCtLdB5UHy0P6kALpMBpruC9834RVHWyAY29UVkxXCPunyMkDxYOAbGAK0t0HlQfLQ/pQAup6n3HTLipx3qaZqGzr/JCBAU3au7GTaGKBdXgYoHgx8A0OA9jaoiXHv9qcE0EUUJQ9wbr7iPNBa1QQjD9CfdXkZoHgw8A0MAdrboCbGvdufEEDTRzmdVhGfha9RTTD2eqDuB1q+DFA8GPgGhgDtbVAT497tTwig6WIiB197kt5oywF0ew3Ufr7tywDFg4FvYAjQ3gY1Me7dPgHqS3qjLQfQ7V14+/m2LwMUDwa+gSFAexvUxLh3+xMCqB3Cb+8ccQhfq5pgJKRcJPNATYeXAYoHA9/AEKC9DWpi3Lv9CQHU3kRK73G8nfMmUp1qgpF2NRd2R151eRmgeDDwDQwB2tugJsa9258SQFduEZGTkxO3GhOnMVULPhj4BoYA7W0gHWP888Sq4UT6Re6lnJxIXyP4YOAbGAK0t4F0jPHPE6umy9l9lfHzoy61VG9D558kQEWCgW9gCNDeBtIxxj9PrBovJnL9/L59t/HDV11KqdmGzj9JgIoEA9/AEKC9DaRjjH+eWHE1Jv/twwcD38AQoL0NpGOMf55YEaD+25c+cOLBwDcwBGhvg8qD5KF9AtSX9EabAAU1MARob4PKg+Sh/WkA9ObxyYPX8R95PeCTSJWSPnDiwcA3MARob4PKg+Sh/YkA1D65mbxMLhMf5ayW9IETDwa+gSFAextUHiQP7ROgvqQ32gQoqIEhQHsbVB4kD+0ToL6kN9oEKKiBIUB7G1QeJA/tTwOgIaQ32gQoqIEhQHsbVB4kD+0ToL6kN9oEKKiBIUB7G1QeJA/tE6A5bS524/rrx/Ew/8H2iaU38yh6mC7UdnNWuOSQ3mgToKAGhgDtbVB5kDy0T4DmtNxdGF0lF0pn5+k/5K6aLosXItEbbQIU1MAQoL0NKg+Sh/anAdBbU0BL5oEud3eW1vPo6JW5vkjWt7w5iz6+vD5L3qhU0gFVHG0CFNTAEKC9DSoPkof2JwLQ/RvwhXfh3VJN279cpK/vvHC9zZX7+2XydyUdUMXRJkBBDQwB2tug8iB5aJ8ATfX2URSdbP8y/nwyds+jM/l6PS+Z/6Q32gQoqIGZBEBvPt2+2yA5y9xAb1XQSyFApVLWAaCzDz+/vPWJ2bPVFqCrfZLmAbrIXo18extabO++CFCRYOAbmEkAdOEuiS3d27Xsawrty2JuflHQSyFApVLW9CbSMoo+/tEko/VbLz+7OfvocsfN5faf4zH86V4/tLQDqjjaBCiogZkAQG3PZfd+19fr91/b12sti3opBKhUypq/E2l7XJbR7Y7kjybX8VzkAHq8dxOptAOqONoEKKiBmQBAV8fu9YTbt2QnAC3sgGICNH+B4qrqAkWn9j2lrPFrjfPvhS94/24OoNsKk6+SaUz3Xld0QBVHmwAFNTATAGjKzpU9r2KAJkP4wg4oJkDzFyiuqi5QdGrfU8qaAdQ9EF/w9U6lADVv7kdRPMYv6oD+Yaurcan7gdPR/ggMYh0AtPmPouin/+3XV1f/41/9/urqrz//2dX/iKKf/fV///3/G/3dr720XnmQxA3++nNXx19//okr8ad/8/u//eqTq3/+ma/2PaXsIGZlAM3eZbwqXEykHKDZB+xl0EdxZ/TwpUp6+wbsgYIamOn1QN3fLE/jkfzqYIgI2APdv0DxSdUFik7te0pZ4yF8NHvmvrRPZrYZwu9aOLdz7HePKOW3ocX27osAFQkGvoGZDkC310DtX8SAiYe87m/6G1QeJA/t1xnkfz18UnWBolP7nlLW8CZSciXz5OTE/begirKbSLt/j//a/ttmcchfvdEmQEENzHQAur0Lb/9icW7GCdCfuZtI8e+HRVT8NGPr9j2lrCFAbRc0U0EHtHQa0+7nz9OZoev5wR7QG20CFNTATAegZpHMA7Xff3BpxjKEv9UDdT9QcoGiU/ueUtZ0HmiOoB8VbX/ZRPrsn91E0GTaWnR7DK832gQoqIGZEEDtRB/HTzsN1HZhDvtouADdXgO1ny+7QNGpfU8pa74a0/fP78f0vPvw8B6Q1SE3V7l7Ta4DSoD6OnDiwcA3MJMAqKxB5UHy0H4jgKYXKH5vP192gaJT+55S5ms5u1XJYiLbf7Vfcwjv6cCJBwPfwBCgvQ0qD5KH9hsBNL1AYQ1KL1B0at9TyvwDdH85O6d0GbuYqbyJRIAGMTAEaG+DyoPkof1mAE0uUFxVXaDo1L6nlHUA6ObtLyvmgRrzZm9BZavtMnbpNKaDHaA32gQoqIEhQHsbVB4kD+1P6Vn45DUdFW/lzF/x3H+lR34d5WtOpCdAgxgYArS3QeVB8tD+lAC6v6gdX2tcLekDJx4MfAMzfoBW7sPABgRopqqJ9ARoM0kfOPFg4BsYAjSkAQGaqfRRzuju/fj/9lGkZ12KqdyGzj9JgIoEA9/AEKAhDQjQTKWLiTxL1pfKr2znS3qjTYCCGhgCNKTBRM8TqzbL2S3tw5nrecmr4bpLb7QnGgx8A0OAhjSY6Hli1Qagq3SN+cYzWZtuQ+efJEBFgoFvYAjQkAYTPU+sGl8DPU1fq1m8HmgvaYu2vWLhXh/Q/i0CTvDBwDcwBGhIAwI0U/ld+DuvY47e/Ww+eoBuLuzTY6f2Mdyf2r5FwAk+GPgGhgANaUCAZiqfBxpjM53NNPJroKvZi+1rDv9W9prDSsEHA9/AEKAhDQjQTGXL2T23AE3m0zd/GLXpNnT+SRmA2h72+r0XCUDbd0Dxg4FvYAjQkAYEaKbSZ+Hf/dNl+iymb362jnbfa5Q1Snqg0XkyhG/fAcUPBr6BIUBDGhCgmWoXE/F8B95tQ7uP975GaaoZfHN2nKxg2uEtAk7wwcA3MARoSAMCNFMxQP/yVICbu21o9/He1yj3GFzwtumYrrP/262lddX2LQJO8MHANzAEaEgDAjRT2TQm33M/97ah3cd7X6PcY/CmmMHJYoRXbd8i4AQfDHwDQ4CGNCBAM1VNpJdSpx5oj2uUeww+fNu0w+XS/ca4avsWASf4YOAbGAI0pAEBmqmsB+r91nt+G9p9vPc1yj0GH75t2r7icD1360H/1PYtAk7wwcA3MARoSAMCNFPxNdBV4ULIvtThLnyva5R1DLZ3mBw/N79q+xYBJ/hg4BsYAjSkAQGaqbgH+t1zOwH07kmiBwqeROpzjdLkGVz5tumJBgPfwBCgIQ0mep5YNV7OTs+Cyr2vUebbqX7b9ESDgW9gCNCQBhM9T6wAAdr7GuUegyvfNj3RYOAbGAI0pMFEzxMrQID2vka5x+DKt01PNBj4BoYADWkw0fPEytN74XtpgGjvGFz5tumJBgPfwBCgIQ0mep5YTRSgpULAg3gw8A0MARrSgADNRIDqx4N4MPANDAEa0oAAzUSA6seDeDDwDQwBGtKAAM1EgOrHg3gw8A0MARrSgADNpBygvsoWNYAPBr6BIUBDGhCgmSYAUPH9Ch8MfANDgMob2HdN2tl/k335ohUB6n+/wgcD38AQoOIGdlFdszi6nO7LF60IUP/7FT4Y+AaGABU3cA/vZevyTvHli1ajA2j9uEJ8v8IHA9/AEKDiBvsAneLLF63GBtDcuKLgZR1O4vsVPhj4BoYAFTfYH8JP8eWLVo0Buvnj47l7tfG/b9F6w21o/tHasnO/Fkte1oGPB/gCCFAPBpX7MIzBYjfYm+TLF62aAvQv83QZkVXU5R0a1dvQ/KO1Ze8DtHBcIb5f4YOBb2AIUHGDZFVyt7LQRF++aNUQoMtsHaYOL9Co3YbmH60te38IXziuEN+v8MHANzAEqLjB9sViZrIvX7Rq/EqPKLpvAWrv0bSd71W7Dc0/2qDs2nGF+H6FDwa+gSFAxQ3yAJ3myxetmgHUQulV8m7OZeT9Fcc+AZobV5S9rEN8v8IHA9/AEKDiBvZUs4M9M9mXL1q1eCtnAlCBVxz7BGju12LZyzrE9yt8MPANDAEqb2AX1XXInOrLF61avBceDqAl4wrx/QofDHwDQ4CGNOCz8JnKAJr1QNdz1QDNjSvKXtYhvl/hg4FvYAjQkAYEaKayIXxkb2wj3ETajSvKXtYhvl/hg4FvYAjQkAYEaKaKu/An82j29/aL8xbtN9qG5h+FOHvhg4FvYAjQkAYEaKaSeaCL3Es5fd+EJ0C1BQPfwBCgIQ0I0EwlAN18JcdPAlRbMPANDAEqa4CQghApa/4s/PePHT4ffNmi8abb0PyjCMcNPxj4BoYAlTVASEGIlLVbjemHFg232YbmH0U4bvjBwDcwBKisAUIKQqSsIUD/8tT7uD2/Dc0/Wln2/6zSrg0GY/wGhgCVNUBIQYiUNZ7G5P/KZ24bmn+0smwCFKQAAtSDQeU+FDdASEGIlLV5EklMBKiyYOAbGAJU1gAhBSFS1uZZeDERoMqCgW9gCFBZA4QUhEhZ84n09161aLWdCFBlwcA3MASorAFCCkKkrGEP9LvndgrT3ZNED5Q+C0+AghRAgHowqNyH4gYIKQiRssbXQPPSupgIAQpSAAHqwaByH4obIKQgRMoIUAZjjAaGAJU1QEhBiJQRoAzGGA0MASprgJCCECkb1XvhCVCQAghQDwaV+1DcACEFIVJGgDIYYzQwBKisAUIKQqSMAGUwxmhgCFBZA4QUhEhZB4Bu3v6S10ArBR8MfANDgMoaIKQgRMqaAvT6MW8iTScY+AaGAJU1QEhBiJQ1BOj+bXgCtFrwwcA3MASorAFCCkKkrCFAl5zGNKVg4BsYAlTWACEFIVLW/K2c0d379mHO+ItnLZpvtg3NP1pZNgEKUgAB6sGgch+KGyCkIETKmk+kf2bMYvbCstT7ynYEqLJg4BsYAlTWACEFIVLWaj3QZXRqzHqu9r3wBChIAQSoB4PKfShugJCCEClrBdCVRWf8te/V6QlQZcHANzAEqKwBQgpCpKz5NVDX+YwxuuJNpDrBBwPfwBCgsgYIKQiRshZ34e+8jjl697M5AVon+GDgGxgCVNYAIQUhUtZiHmiMzXQ2E6+BVgs+GPgGhgCVNUBIQYiUNX0SafPcAjSZT+/99UgEqLJg4BsYAlTWACEFIVLW/Fn4d/90acz1oyi65/31cgSosmDgGxgCVNYAIQUhUtZ+MRGB98MToMqCgW9gCFBZA4QUhEgZl7NjMMZoYAhQWQOEFIRIGQHKYIzRwBCgsgYIKQiRsuYA/f6bb377JNHnnEhfKfhg4BsYAlTWACEFIVLWFKBv5lyNaTrBwDcwBKisAUIKQqSsIUBXXM5uSsHANzAEqKwBQgpCpKzFcnYE6GSCgW9gCFBZA4QUhEhZ8+Xs7v3u2x9S/dii/Ubb0PyjlWUToCAFEKAeDCr3obgBQgpCpKwxQL0/fpTfhuYfrSybAAUpgAD1YFC5D8UNEFIQImWNh/DeV1HOb0Pzj1aWTYCCFECAejCo3IfiBggpCJGyxjeR2AOdUDDwDQwBKmuAkIIQKWs6jWkR3XnVotV2IkCVBQPfwBCgsgYIKQiRslqA3jw+sbpvV2E6SfWAd+ErBR8MfANDgMoaIKQgRMrqAbr3RnhOY2oi+GDgGxgCdDt5+9x+cWq/b7mOL3wKQqSMAGUwxmhgCNBEi6PLm7Pzn957YW5+0fK0hU9BiJQRoAzGGA0MAeq0mr0w6/df/+3i3CxPfRogpCBEyrgaE4MxRgNDgFptLuJhewLQ1h1Q/BSESBkBymCM0cAQoFa2A2qSIXzrDih+CkKkTClAS69/V5ZNgIIUQIB6MKjch+lnFu68sSdR3AFdtJzMDZ+CEClrCdDvf/Pkqf93epREO7n+vd67/l1ZNgEKUgAB6sGgch8mH4lPn63B8jQeya+O2py88CkIkbKmALUYS14PL/BMUnG00+vfm73r35VlE6AgBRCgHgwq92HykdX2du9V3AVZHl0mZ7EXA4QUhEhZQ4Cu53YkvZ6L3IQvjvb2+vdm7/p3ZdkEKEgBBKgHg8p9mHxkue1xXi3ODQEqkbKGAI27nvGxWKTTmFpfjq7bhqK/3F7/Xu9d/64smwAFKYAA9WBQuQ+Tjyy2tw5++uDScAgvkbLmCyofX9opoceXXzuWelVhtIuvf1eWTYCCFECAejCo3IfuE5uLtOex+ZW9FrrkTST/KWuzHqgdwZ/bP0NMpM+uf5u969+VZROgIAUQoB4MKvehuAFCCkKkrDFAY2jGvcEYo8nXXlUU7ez6t9m7/l1ZNgEKUgAB6sGgch+KGyCkIETK2gB04e4fBQJodv3b7F3/riybAAUpYDoAXSQXvGqW8yBARQxCpKzFEN5dAnW34kNcA82uf6/3rn9Xlk2AghQwGYAuTounM/swqNyH4gYIKQiRsoZ34e0v0ufu0aDt71SfKoj27vr3xd7178qyCVCQAqYCUDdsiv84mM7sw6ByH4obIKQgRMravRc+HrvbG/JBpjEVq7JsAhSkgKkAdDtt6GA6sw8D/IMkbRAiZQ0Bmr4Y3pKz7WSIJtvQ/KOVZROgIAVMBaDL41V63+DWdGYfBvgHSdogRMqaPsq5+XoezZ7Zr1beO6AEqLZg4BsYFQBd2JsGthtas5wHASpiECJl7Vdj2vzQovWG29D8o5VlE6AgBUwGoHYIv53RXLGcBwEqYhAiZUqXsytVZdkEKEgBUwHo0k4lSQFatZwHASpiECJlCgHavWwCFKSAqQDUPQ1y86ljZtVyHgSoiEGIlBGgDMYYDYwKgLrJeMkjIeuq5TwIUBGDECkjQBmMMRoYFQB1s1ccMm9NZ/ZhgH+QpA1CpIwAZTDGaGB0AFTSAP8gSRuESBkBymCM0cAQoPoPkrRBiJQRoAzGGA0MAar/IEkbhEgZAcpgjNHAEKD6D5K0QYiUEaAMxhgNDAGq/yBJG4RIWXOAbv742K1Ff/PvW7TecBv2vuteNgEKUsAUAFpZYvKRZouFdjKAOEjSBuIFWDUF6F/m6XJM4s/Cdy+bAAUpgAC12i0W+lPVYqGdDCAOkrSBeAFWDQG6zNazK1sQoYcIUGXBwDcw+gGaWyz0b1WLhXYygDhI0gbiBVi1WA/0vgWoe0Fni/YbbcPed93LJkBBCiBAzd5ioX+rWiy0kwHEQZI2EC/AqvmK9K+StyEtpVek7142AQpSAAFq9hYL/alqsdBOBhAHSdpAvACrpu+Fz97HKf5Sue5lE6AgBRCgpvlioZ0MIA6StIF4AVYt3spJgDasBz4Y+AYGAKC7xUKvqhYL7WQAcZCkDcQLsGr+Vs4Unes5AVoj+GDgGxj9AM0tFnpVtVhoJwOIgyRtIF6AVdMhfGTnW/AmUiPBBwPfwOgHaG6x0KuqxUI7GUAcJGkD8QKsWtyFP5lHs7+PkpcbexUBqiwY+AZGP0Bzi4X+VLVYaCcDiIMkbSBegFXDeaCLaCffN+HBAFr//Ah8MPANjH6A5hYL/VXVYqGdDCAOkrSBeAFWTd/K+ZUcP7EAunt+ZF32/Ah8MPANDABAJQ0gDpK0gXgBVo2fhf/+scPngy9bNN50G/a+6152CIDmnh/ZlD0/Ah8MfANDgOo/SNIG4gVYtVqNyf8bjZNt2Puue9khAJp7fmRT9vwIfDDwDQwBqv8gSRuIF2DV7C78b558vh24rx/f/6hF+422Ye+77mWHAGju+ZF12fMj8MHANzAEqP6DJG0gXoBVi4n02ddTfpSzyfMj8MHANzAEqP6DJG0gXoBVa4BOfCJ97vkRU/b8CHww8A2MboBC7EN4A/ECrGoBmp/AFGWL2nkVEkBzz4+YsudH4IOBb2AIUBqIF2BVC9D1/ACgUx7C554fMWXPj8AHQ7mBi2R6FcWULuVOgE7dQLwAq/oh/GEXdMpPIuWeH1mXPT8CHwzlBuker5qKawhQGogXYFUP0PXjk/sxNe+eJHrw+asWzTfbhr3vupcdAqC550cuyp4fgQ+GcoNl0uWsmoprCFAaiBdg1fomkoCgANpA8MFQbrBIkFk1FdcQoDQQL8Cq9TxQARGgyoKh2+Dm7KR2Kq4hQGkgXoAV3wvPYIAZrOenyQXo6qXcCdCpG4gXYNUCoN9/880337Zouvk27H3XvWwCFKQADwY1U3ENAUoD8QKsmgJ08zKZzjT7hxaNN92Gve+6l02ANmq/fhYQwB6qmYprCFAaiBdg1XQ5uwsuZ6eG0D3br58FJF2A/FRcQ4DSQLwAqw4LKvt+owcBGjYY9bOApAvoOxX3fPtboHQqriFAaSBegFXzV3rMnn77w3e/nU95Iv04AFo/C0i6gJ4GN2e1U3ENAUoD8QKsmgF0kY3cJ/1SuSAAlb5E2WAWUD+DBhI3MAQoDcQLsGr6Vs7sl3x8fk/2WfggAJW+RNlgFlA/gwYSNzD9AdrsiftS4e9DeAPxAqy6rAc62dWYggA0yCXK6llAHgyqJW5g+gN094vsp4on7kuFvw/hDcQLsCJANRnYnw5yibJ6FpAHg2qJG5j+AN39IvtbxRP3pcLfh/AG4gVYNR3CR9sALSe8WjJ5pAAAIABJREFUnF0IgIpfomwwC6ifQQOJG5j+AN39IvtbxRP3pcLfh/AG4gVYNbuJFFMz+tjd9/w62rHUlwjQ3IETv0TZYBZQPwPR22AND4HpDdDcL7KfKp64L1XPfUiD/gbiBVg1A6idOGJXtLvfYEH65f580Tfx2fTwcttM8YSTve+6lz0KgKY7SvASZf0soJ4GkrfBggG04RP3peofAhr0NBAvwKrhRPpV8/WUF3sAXeZfArIsngFFgN4+cNiXKCVvgwUDqFNyHK4qnrgvlfaDNAED8QKsmj4L/2b7Zo/Zs+oG93uZcW/n48vrs2TUX9IBJUDzB24Elyglb4MNAtCKJ+5Lpf0gTcBAvACrxqsxbV7a8fvJ07rfwut5/hf1ynU+l8mQsaQDSoDmD5z8Jcp69TMQvQ0WDKC5X2RXFU/cC+1DGngwEC/Ayvt6oKs9SiboTDC6npdcPSVA8wdO/BJlvfoZiN4GCwbQ3C+ynyqeuBfahzTwYCBegFUdQG9++WW7SUvLvWukeYAuym7fE6DKguHBQOo2WDCA5n6R/ariiftSIRykkRuIF2BVC9A4Rg++bNHg4s5/eD6PfyZZejk3hC/tgBKgwYIRrgCp22DhALoTn4XHNBAvwKoJQKMWDE0/v50tmruJVNoBJUCDBSNIAaK3wQhQGiACNNbDRq/zWM+j2dNL8+6rlKDJNKZ7rys6oARosGAEKUD0NhgBSgMogJq3z7MJoLOH9a+EX23niS7SyZ9v7kfRR5eFHdA/bHWVV/eyK08uCIOrRtJewF9/HkX/y7/EX/ztV5/Ef/5z9He/Dl1BrAOANtu5viQdAhoM3r5Tk7vw3+cY+vTHWoYmujnbI+bKXQZ9FHdGDyHMHmg7A8UFNAyHeAWGPVAaiBdg1XAa0/fP5y0ZurnIz2eKvztPnpGOZgdPMhGg7QwUF9AkGSEqMP0A6qFGxftwKgbiBVg1nwd6nTG02XJ2izxAXQd0Ef+xWRwu5kSAtjNQXECTYISowBCgNBAvwKrVRPpr90qkDgB1HdB4UH9u7zIdzKcjQNsZKC6gSTBCVGAIUBqIF2DVCqDvXjZZjilRzMzdNdDkCqhDZ4LR/W3Y+6572Ur4RoDWSrwCQ4DSQLwAq+YAffeyyRA+G6Gv5ztQug4oAerLAL4AArR/+zQYvH2nhgDdXQCN3DzPUq3SJ95iZu6udSbPx3MI78kAvgACtH/7NBi8faeW05hq78HbF3g+i4H7KLduaLqMnWMqbyL1N4AvgADt3z4NBm/fqc1E+kYzmCw6b60bul3GLp3GdLAmAwHazgC+AAK0f/s0GLx9p+aPct5tOIfeLRyaf2hpt47yNSfSE6BBDAwBSgPxAqwaArQpPTuJAG1nAF8AAdq/fRoM3r5TE4CK0tMQoG0N4AsgQPu3T4PB23eqBehjYXoaArStAXwBBGj/9mkwePtO3l/p0UEEaDsD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0P7t02Dw9p0IUDwD+AII0Nr2NxdRdBr/d5X+59i3gXgFwxuIF2BFgOIZwBdAgNa2vzg2N2en8f/P1++9MDe/eO3bQLyC4Q3EC7AiQPEM4AsgQOvat9Q0y6PL9fuvNxfnZnnq20C8AgUG4gVYEaB4BvAFEKCN2s8AWtQBxecbAepLBGg7A/gCCNAm7cfD93QIX9QBxecbAepLBGg7A/gCCND69jcX0dFlchMp7oAuotkLzwbiFQxuIF6AFQGKZwBfAAHaqP3VnWTkvjyNR/Iri1PPBuIVDGsgXoAVAYpnAF8AAdqofTuGt//5xWt7NfS9F94NxCsY1kC8ACsCFM8AvgACtK591/lMAbo4NwRo+EPQUAQongF8AQRoXft27pJJRu3rDy4Nh/DhD0FDEaB4BvAF4ANU/EEha+CQ6VBqlryJ1N5AvAArAhTPAL4AfIDyQSEAA/ECrAhQPAP4AuABygeFEAzEC7AiQPEM4AvQAtDSgXjlJmw/xAeFlBuIF2BFgOIZwBegBaClA/HKTUg/wweFtBuIF2BFgOIZwBegBKDlA/HKTXCf4INC+g16tl9/o9CKAMUzgC9ACUCdCgfilZuw/ZDUg0Li+7B4RxxIuYH4RIj6G4VWBCieAXwBigBaPBCv3IT8jxqBB4XE92HhjjiUcgPpiRANbhRaEaB4BvAFqAFo2UC8chOM8INC4vuwYEcUSbdBmIkQ1TcKrQhQPAP4AtQA1JQMxCs3wQg/KCS+D4t3xIEADKQnQtTcKLQiQPEM4AvQBNDCgXjlJtgPSD4oJL4Pi3fEgfQbCE+EqL1RaEWA4hnAF6AEoOUD8cpNaJRpzfuwWQE9DW7Oosju34p72D0rCDERovpGoRUBimcAX4ASgJYPxCs3oVmoFe/DZgX0M7g5i4m5OLqsuofdvwLxFVOrbxRaEaB4BvAFKAFo+UC8chMaJFr3PnQ/Xt9D7GWwsrsyJk7VPeyeFRjZFVMb3Ci0IkDxDOAL0ALQUlVuQrMmFO9D+9MNeoj9DKwygBbfw+5lIL5iaoMbhVYEKJ4BfAEE6KD70P50gx5iPwOrDNDF97B7GcivmFp/o9CKAMUzgC8AG6DwBylrorqH2NtgaYlTcQ+7n4GCFVOtCFA8A/gCCNBB92HWRHUPsa/BMjpPWyq5h+2hgkpJt+9EgOIZwBdAgA66D7ct1PQQexossgbL7mH3r6Ba0u07EaB4BvAFaADohA9S2kBdD7GfwfJOdlWg7B527wpqJN2+EwGKZwBfAAE66D5Mfr62h9jLINda6T3svhXUSbp9JwIUzwC+AAJ00H3ofry+h9jLYBk5nVfdw+5ZQa3ED0FRzAhQ/QbwBRCgg+5D+9MNeoj9DBoI/hAUxYwA1W8AXwABOug+tD/doIfYz6CB4A9BUcwIUP0G8AUQoIPuw+qzEcFAySFwsSJA4QzgCyBAB92H1WcjgoGSQ+BiRYDCGcAXQIAOug+rz0YEAyWHwMWKAIUzgC+AAB10H1afjQgVKDkERTEjQPUbwBdAgA66D6vPRoQKlByCopgRoPoN4AsgQBH2IbyBeAFFMSNA9RvAF0CAIuxDeAPxAopiRoDqN4AvgABF2IfwBuIFFMWMANVvAF8AAYqwD+ENxAsoihkBqt8AvgACFGEfwhuIF1AUMwJUvwF8AQQowj6ENxAvoChmBKh+A/gCCFCEfQhvIF5AUcwIUP0G8AUQoAj7EN5AvICimBGg+g3gCyBAEfYhvIF4AUUxI0D1G8AXQIAi7EN4A/ECimJGgOo3gC+AAEXYh/AG4gUUxYwA1W8AXwABirAP4Q3ECyiKGQGq3wC+AAIUYR/CG4gXUBQzAlS/AXwBBCjCPoQ3EC+gKGYEqH4D+AIIUIR9CG8gXkBRzAhQ/QbwBRCgCPsQ3kC8gKKYEaD6DeALIEAR9iG8gXgBRTEjQPUbwBdAgCLsQ3gD8QKKYkaA6jeAL4AARdiH8AbiBRTFjADVbwBfAAGKsA/hDcQLKIoZAarfAL4AAhRhH8IbiBdQFDMCVL8BfAEEKMI+hDcQL6AoZgSofgP4AghQhH0IbyBeQFHMCFD9BvAFEKAI+xDeQLyAopgRoPoN4AsgQBH2IbyBeAFFMSNA9RvAF0CAIuxDeAPxAopiRoDqN4AvgABF2IfwBuIFFMWMANVvAF8AAYqwD+ENxAsoihkBqt8AvgACFGEfwhuIF1AUMwJUvwF8AQQowj6ENxAvoChmBKh+A/gCCFCEfQhvIF5AUcwIUP0G8AUQoAj7EN5AvICimBGg+g3gCyBAEfYhvIF4AUUxI0D1G8AXQIAi7EN4A/ECimJGgOo3gC+AAEXYh/AG4gUUxYwA1W8AXwABirAP4Q3ECyiKGQGq3wC+AAIUYR/CG4gXUBQzAlS/AXwBBCjCPoQ3EC+gKGYEqH4D+AIIUIR9CG8gXkBRzAhQ/QbwBRCgCPsQ3kC8gKKYEaD6DeALIEAR9iG8gXgBRTEjQPUbwBdAgCLsQ3gD8QKKYkaA6jeAL4AARdiH8AbiBRTFjADVbwBfAAGKsA/hDcQLKIoZAarfAL4AAhRhH8IbiBdQFDMCVL8BfAEEKMI+hDcQL6AoZgSofgP4AghQhH0IbyBeQFHMCFD9BvAFEKAI+xDeQLyAopgRoPoN4AsgQBH2IbyBeAFFMSNA9RvAF0CAIuxDeAPxAopiRoDqN4AvgABF2IfwBuIFFMWMANVvAF8AAYqwD+ENxAsoihkBqt8AvgACFGEfwhuIF1AUMwJUvwF8AQQowj6ENxAvoChmBKh+A/gCCFCEfQhvIF5AUcwIUP0G8AUQoAj7EN5AvICimBGg+g3gCyBAEfYhvIF4AUUxI0D1G8AXQIAi7EN4A/ECimJGgOo3gC+AAEXYh/AG4gUUxYwA1W8AXwABirAP4Q3ECyiKGQGq3wC+AAIUYR/CG4gXUBQzAlS/AXwBBCjCPoQ3EC+gKGYEqH4D+AIIUIR9CG8gXkBRzAhQ/QbwBRCgCPsQ3kC8gKKYEaD6DeALIEAR9iG8gXgBRTEjQPUbwBdAgCLsQ3gD8QKKYkaA6jeAL4AARdiH8AbiBRTFjADVbwBfAAGKsA/hDcQLKIoZAarfAL4AAhRhH8IbiBdQFDMCVL8BfAEEKMI+hDcQL6AoZgSofgP4AghQhH0IbyBeQFHMPAD0+nEURQ9epd+9mUfRw8vk65uz2YuCHyBAlQUD38AQoDQQL6AoZv0BuoqcZufuu6X75s7r9Jvjop8gQJUFA9/AEKA0EC+gKGa9AbqeR0evzPVF5DqbN2fRx5fXZ9GpMaUdUAJUWzDwDQwBSgPxAopi1hugi+jIDtg3F663uXKdz2XydyUdUAJUWzDwDQwBSgPxAopi1hegcZczGbvn0Zl8vZ6nI/nbIkCVBQPfwBCgNBAvoChmfQG62l7vTEiaB+giGcgfigBVFgx8A0OA0kC8gKKY9QVoOlp3Y/jTvX5oaQeUANUWDHwDQ4DSQLyAopj1BegiB9DjvZtIpR1QAlRbMPANDAFKA/ECimLWH6DHe18l05juva7ogBKg2oKBb2AIUBqIF1AUM98ANW/uR9FHl4Ud0D9sdUVRvnUA0KE3iBqjxAGaauUugz6KO6OvDn7mDwd/01RXnX9SR/s0kGu/pgcaYhMmYwBfgL+YCQF0cxGd2zn2u0eU8tvQ2Y3BGL0BAQpgAF+AJoDu3UTaynVA7b9tsg/ktqGzG4MxegMCFMAAvgA9AL01jSmV64AmM0PX84PnOfVGW+9xm4wBAQpgAF+AHoDemkif/a2bCGrRuff36TZ0dmMwRm9AgAIYwBegB6C3HuVM5DqgBCgNQrZPgIYzgC9AD0BvLSaSaOW+5hCeBgHbJ0DDGcAXoAig+8vZOaXL2MVM5U0kGoRqnwANZwBfgCKAmjd7CypbbZexS6cxHawJqjfaeo/bZAwIUAAD+AI0AfTWKz3y6yhfcyI9DUK1T4CGM4AvQBVA229D559kMEZvQIACGMAXQIDKSO9xm4wBAQpgAF8AASojvcdtMgYEKIABfAEEqIz0HrfJGBCgAAbwBRCgMtJ73CZjQIACGMAXQIDKSO9xm4wBAQpgAF8AASojvcdtMgYEKIABfAEEqIz0HrfJGBCgAAbwBRCgMtJ73CZjQIACGMAXQIDKSO9xm4wBAQpgAF8AASojvcdtMgYEKIABfAEEqIz0HrfJGBCgAAbwBRCgMtJ73CZjQIACGMAXQIDKSO9xm4wBAQpgAF8AASojvcdtMgYEKIABfAEEqIz0HrfJGBCgAAbwBRCgMtJ73CZjQIACGMAXQIDKSO9xm4wBAQpgAF8AASojvcdtMgYEKIABfAHgAKUoATFlVADtx4wApcYipowKoP2YDQHQ7uo+LNPRPg0Gb1/DJsAbwBfgz4AADdk+DQZvX8MmwBvAF0CAYrZPg8Hb17AJ8AbwBRCgmO3TYPD2NWwCvAF8AQQoZvs0GLx9DZsAbwBfAAGK2T4NBm9fwybAG8AXMFWAUhRFKRIBSlEU1VEEKEVRVEcRoBRFUR1FgFIURXUUAUpRFNVRMADdXMyeYRtQAJJOAVM2MsEA9OZs9gLbINPmu2/+jNy+hEHc5IH+fOnZpIGkUzCelCEaCKQMCKB3XmMbxHr7715boyiKZE4j6fblDFybtyR/PIq2Q9Z1DCnDNRBIGQxAzSI6BTeIHeKjtbkQo4N0+4IGWgAqngL8lAEbTBqgm6+i2eeSwztxg5U7Wut5dGyuLwTOI+n2RQ3e/XCgH70aNJR0CuBTBm3gP2UwAL31y8P/bz5xg7TzsXTDkvX8yPu5I92+vMHwkk4BfspGYOBTBGhAA5uIeGRiIyFwLUy6fXkDBYIHKH4KsGIGA9BbvW+B4Z20QZKFeGRyaoQAKtu+vIFVdhS+/9P/McSpI50C9JSNwMD4TBkOQOGVZCEZmUgMTaTblzcw5s186JtI6MJPgXzMfKaMAA2mjb0gno5M4v8co7Uvb2C7HTkpv/qlU/gpEDfwmjIogIoP72QNVlF0Nz52cTpeziVm0Em3L2+wiGZP/3Q2e/rNy0fR7Nx/+80kHTPslOEbeE0ZEEDFh3fiBl+lv/HsnQSJ2YDS7UsbxP2Nc5tvG+o3Q43gpVMAnzJ0A78pwwGo+PAuwPjx7ZMnX9pbi58+fCXQunz7wgbJ/ddlMmpbis84L5R0CkaQMnADvynDAaj48E7H+HHKSm4frBKsDDQDUDoFTNnQ8psyGICKD+9UjB+nre0EFrfzh5kBKJ0Cpmxw+U0ZDEDFh3fy48fvfvvkJNaTL4SWGZJuX9xgc2EPQbpg0TAAlU4BfsrgDfymDAigwsM7YYPr5/lrX/6v7Ui3L29gLFHsbl84vCwHmcYkHTPwlI3AwHPKwAAqOLyTNXD3Xmfxr9X4l6v70vPlL+n25Q2s1nM7ql1F0b0njyOJiaa1ko4ZdspGYGA8pwwGoOLDO1GD+Jjd+3L37dtHnue3Sbcvb5Bo5ciyEJviUyvpmEGnbAQGTj5TBgNQ+eGdpMHiVnu+n7CQbl/eYNvuP1mbN/ej2UfDPIckHTPklI3AIG3WX8pwACo+vBM0OHyRQzqIA2lf3kCNpGMGnLIRGPgXDkDlh3dyBodjNb+jN+n25Q30SDpmuCkbgYF/AQFUfngnZsAeaJ023/358vY7v4Z4qZyRjxlsyvANBFKGBFBgLaJj4Wugsu2LG7iehvxy7eMWfAqkDQRSRoAGEe/C14gA9SD4FEgbTBmgN2d3n4oO6WQNVrfmt0XPsNoXN3hnF2cXf+tAraRjhp0yeAP/KQMCqN2zXwgt/yJvsP+ExQPhJ5H8ty9voELSMQNP2QgMPAsGoMZ8/9z9dnr4LapB8ozvA+Fn4eXalzfQIOkUoKdsBAY+BQTQWO9e3re/lx5+Wf9RpQZUrYZ9L7yVdAqYsuE1uSH8Vpu3n9nwyS00IW5AlWtvADfkTSTpFDBlA8pnyuAAmv7+ljy5/BvcPA53MefdyydPnop03jZ/+s1n8djqwye/k+ob7i/XPuxdeOmYYacMOWZeUwYG0OuXj13Vs4dCv7plDG7OopnweO3toyi698o+X+3kfw749aNc6mYyt5IX8bj2d0NPpDfyMYNN2Shi5jVlQAD9fpu6D7+UibWcgbv3KvUCGack0LMXq/jPk5PI/1Pc9tf2yYdxy/c+s81H9wT6ZvFuGuQ9SHuSjhlyykYRM78pgwFoMv119uHvpLolkgY3Z7O/F/l9vZV9E+zTPz6K7p1FH9vXaX8dRX4XUrxJGrYtn9rx21zi8p2CB5+lYwadsnHEzG/KoAA6k7noIm9gj5lbKvYjoQLSR+AWWZdg6blvsHv7xNI9G3It0VlUAlDJmEGnbBwxmyhAN8+TS78P/lEoHZIG7pht3Ouu70kMDLeLMKzn2yfffK/1cZE9Ureeu3NmJdEFXXju0LSXdMyQUzaWmHlNGQxAjZ36IZ1uKYP0l947F+54FPSt96tfSY53v1zl1hlLvxTpLQ70KuN9SccMNmVjiZnXlCEB1GrzXZI+sbGeiEEWg82b++kNxpMPBJazE+sa5NYZk3zr8Ls30eyhgrvw4jGDTNlYYuY1ZWgAtbLzYEUvlnk3yMfgXXoX1qtB8ooIe3EqvWTk+eJUblmx9K0LEmMrXasxSccML2XjiNlEV2NKtXn7mXAPVMLg9u/Rd9/85rMHPg3sKyJ+9yd3e9RFe+n79mhscGQnyNgbr7HD5uVc5CaSFoBKxwwzZeOI2XQBmrt4JLQivZSB/O3l5LrX7EUc6dkD2/nwPUHPvoPirp2gZ7sENoMSVyuHX87OyMcMOGXjiNk0l7Pb3r6cfSG0jI2kQYBo2/kr915sI+5/MuAmbTiyD8/EyZZ6FGxgSccMPGWM2W3BANR1vB8IPYMkbbD5zefBgvD9b05OPpd4HOXdy89OHiQrWW7+q/Zcd5V0zMaSMsYsFQ5AHz+VXSlB3IBqpmFXpBdOAVOmRNMbwl/MngUxeid93oobAGvw5exCxYwpG1CTXM7u8I2nUj7C562C5xXVavjl7ALFjCkbUNNczi5UIhjtATX8cnaBjg5TNqAmupzdIsxSZ4z2cNKwnF2YmDFlw2miy9nZCQ6zz+V7J4z2cNKwa8LEjCkbThNdjSnUUyqM9nBSsGsCxYwpG04EKAE6Vg2/nB0BOn5NdDm7QI/5MdoDSsFydmFixpQNqEkvZycvztAbTmqWsxMXUzacJr+cXQhtfhh6CyYoPasxBRJTNoCmuxqTuX5+39Z780uJp+H2Bj2eF4pN9N1vn5zEevLFmLtWfaQEoJIxY8oG12QBmizUYpN9JnGvYS/a/i8h7T0+JvzyWVhpWM5ONmZM2fCa5nJ27gGCe/+n/Z29uYg8P2/38smTJ5/NZx8+2eq+7+6Pe1viLO4YxN0D9+XQt5upEsnFjCkboXAAurJxTn5nx9H2+7jI/tOxTt4N7n25+/btI9+/AihPEowZUzZC4QDUPWOXDnq8vynlq5OT+1F092SrB0/9tr+4tcG5l79QexK9zt1AkjFjyrTIX8pgAJoskyP4slPJmXOHa/yI3D/Al/B17npJx4wpUyCfKQMCqM2CIEAl1/M+3F5OdC6U4HXuZpKOGVOmQD5TBgTQXNcA7Tcr+wbNJHmdu5mQY8aUNZPXlMEAdO/i1ELy2s7mu2/+7LvNeIN5dapeote522+CZMyYssHkNWU4AF1F0bOkbHsJQ+QC2dt/9zqdZ+t7+Mj7o00kfp27geRjxpQNK78pwwGovXJx97P57P/6LPL/OurUILksIvEUzOrWDL34LKVuS/w6dxNJx4wpG1h+UwYE0O27qCOB11FbrVyg49/ix+ba/wW4/WdEHvAZkQLpuAApGzOmbGj5TRkSQM27l/fjWNwVekIteZnD0g17RNZVS55SfsCnlEsV7Dp3pURjxpQNLq8pgwKoqJJfTPHYyoaa8z8GUYDr3AOLKRteXlMGClCBdcCSNMdjq1PDaA8l8evc7eQ/ZkyZAvlMGQxAxdcBSwySsZXE4GrzX12L1799fPLhP3J0VSLh69y1ko4ZU6ZBHlOGCVCRJ5HsJf10bOV/Al08WHATd9NDN/sHv82PR7LXuWslHTOmTIX8pQwCoPLrgFmt4j06twvkbF7OfU+gs9NWtrNXTj470TFApfYVJGZM2bgEAVDxdcASud/bcdfATnL23P4yGSzE//nYjhnGeosEW2FixpSNShAAFV8HLNXbJ0++tDdHP/U9gEwfuc09eTvcJB2d2nz3zYGCz8MJEzOmbDAJpAwDoAb8jmW6zENutQcu87CvWy+qEXlOp9l24B4XpqxOAimDAajkOmDiOnxuDPlElZAWgCLHjCmr05QBGkTvXp7YZ4m/8H0LeHPhOgXpf6zYN7il/Td9DfhSOXkxZYPJf8rAACp6cm3kJiGmL1tY8uoUhCRjxpSNSkAA3VsoQeI36yJu185heex//oddaOyVu77/zH7L+6N6JR0zpmxUwgHo/iQTmXmgHyddgs3X3pMXNx7d++Kb/+8imiXnzhDLrSuWirvwVtIxY8oG1JTvwtvf3A9/J3ly5cc7/sc+60d7l66Z7H1puYkkHjOmbEBN+SZSXLtsHPbeKCNx9f3759vOzd2nY70/0llaACodM6ZsSE0boMLnk/jD9labH374AXaajKiU3IWXjhlTNqgmfBc+AECF+wYUgOQBypSNSjAANQvpO4qyV6coDEnHjCkbl3AAKvL+g7xE749S1dp89+fL2zdJB7oLLxszpmxACaQMB6Dv3kSzh6Inl52h90Bmhh5VIzfYJa8qAAACXUlEQVR0vnWNf5DxrXjMmLLhJJAyGIAGOLkEnxGhaqQFoPKbwJQNJwJU+OR69/JkHs1OOP8jvN7Zff5OwV14+ZgxZYPJf8pgAKrg5KImIMaMaiMcgAbSO540lLiYsrGIAM1p8+a+G7jd+3LoLaHGK6ZsTEIAaKgpLrkHiYVnTFElun483E2kQDFjygaXz5QhADTQHVrr8OB33/7w/Z8eM9vDSHzFrSqFiRlTNri8powAzbSIcit5cyGbIbTIz8EMPZE+TMyYssHlNWUIAA0zxWVzkXsuZMXOwQCK6TXkozkhYsaUDS6/KYMAaBAFWSeHqtIE9jpTNrj87nUCdCtGe3BtLka/15myweU3ZQRopkXuitSKjykPoeX4V9dgygaX15QRoJluzpJ3cRl7eT+3aiMVTJuL2bOht0FYTNng8poyAnSrzXcv59HdL7755ps/Poqih8MtqDZl3cS7/mSrByMc3zJlCuQzZQToVlreyjNl3Twa+95nyoaX15QRoFsx2sPLrpV58mSrz0fYM2PKhpfXlBGglBrFdPl4hNCkVMlvyghQSo04rYeSF+eBUiPVBOaBUoOL80CpsUr8zasU5TdlBCilR/Ho6tXQ20CNXV5TRoBSauQmST74fNDXGlNjl9+UEaCUGil4Kyc1ek30rZzU+EWAUvIiQCmKolSIAKUoiuooApSiKKqjCFCKoqiOIkApiqI6igClKIrqKAKUoiiqowhQiqKojiJAKYqiOooApSiK6igClKIoqqMIUIqiqI4iQCmKojqKAKUoiuooApSiKKqjCFCKoqiO+v8B1J7gnDKk4lgAAAAASUVORK5CYII=" class="img-fluid figure-img" width="672">
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-fig_b4-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure 4: Voting for a radical right party by cohort, education, and gender (Appendix)
</figcaption>
</figure>
</div>
</div>
</div>
</section>
</section>
<section id="green-vote" class="level1" data-number="3">
<h1 data-number="3"><span class="header-section-number">3</span> Green vote</h1>
<section id="multilevel-model-voting-for-the-greens-two-way-interaction" class="level2" data-number="3.1">
<h2 data-number="3.1" class="anchored" data-anchor-id="multilevel-model-voting-for-the-greens-two-way-interaction"><span class="header-section-number">3.1</span> Multilevel model: Voting for the greens (two-way interaction)</h2>
<div class="cell">
<details class="code-fold">
<summary>Show the code</summary>
<div class="sourceCode cell-code" id="cb7"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="co"># model</span></span>
<span id="cb7-2"><a href="#cb7-2" aria-hidden="true" tabindex="-1"></a>two_green <span class="ot">&lt;-</span> <span class="fu">glmer</span>(</span>
<span id="cb7-3"><a href="#cb7-3" aria-hidden="true" tabindex="-1"></a>  vote_green <span class="sc">~</span></span>
<span id="cb7-4"><a href="#cb7-4" aria-hidden="true" tabindex="-1"></a>    cohort_fct <span class="sc">*</span> edu_fct <span class="sc">+</span></span>
<span id="cb7-5"><a href="#cb7-5" aria-hidden="true" tabindex="-1"></a>    age_grp_fct <span class="sc">*</span> edu_fct <span class="sc">+</span></span>
<span id="cb7-6"><a href="#cb7-6" aria-hidden="true" tabindex="-1"></a>    <span class="fu">factor</span>(essround) <span class="sc">*</span> edu_fct <span class="sc">+</span></span>
<span id="cb7-7"><a href="#cb7-7" aria-hidden="true" tabindex="-1"></a>    gndr_fct <span class="sc">+</span> immigrant <span class="sc">+</span> <span class="fu">factor</span>(cntry_id) <span class="sc">+</span></span>
<span id="cb7-8"><a href="#cb7-8" aria-hidden="true" tabindex="-1"></a>    (<span class="dv">1</span> <span class="sc">|</span> cntry_wave_id),</span>
<span id="cb7-9"><a href="#cb7-9" aria-hidden="true" tabindex="-1"></a>  <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">&quot;logit&quot;</span>),</span>
<span id="cb7-10"><a href="#cb7-10" aria-hidden="true" tabindex="-1"></a>  <span class="at">nAGQ =</span> <span class="dv">0</span>,</span>
<span id="cb7-11"><a href="#cb7-11" aria-hidden="true" tabindex="-1"></a>  <span class="at">data =</span> dfgreen</span>
<span id="cb7-12"><a href="#cb7-12" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb7-13"><a href="#cb7-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-14"><a href="#cb7-14" aria-hidden="true" tabindex="-1"></a><span class="co"># predicted probabilities</span></span>
<span id="cb7-15"><a href="#cb7-15" aria-hidden="true" tabindex="-1"></a>pp_two_green <span class="ot">&lt;-</span> <span class="fu">predictions</span>(two_green,</span>
<span id="cb7-16"><a href="#cb7-16" aria-hidden="true" tabindex="-1"></a>  <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&quot;cohort_fct&quot;</span>, <span class="st">&quot;edu_fct&quot;</span>),</span>
<span id="cb7-17"><a href="#cb7-17" aria-hidden="true" tabindex="-1"></a>  <span class="at">newdata =</span> <span class="fu">datagrid</span>(</span>
<span id="cb7-18"><a href="#cb7-18" aria-hidden="true" tabindex="-1"></a>    <span class="at">cohort_fct =</span> <span class="fu">c</span>(</span>
<span id="cb7-19"><a href="#cb7-19" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;interwar&quot;</span>,</span>
<span id="cb7-20"><a href="#cb7-20" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;post-war&quot;</span>,</span>
<span id="cb7-21"><a href="#cb7-21" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;60-70s&quot;</span>,</span>
<span id="cb7-22"><a href="#cb7-22" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;80s&quot;</span>,</span>
<span id="cb7-23"><a href="#cb7-23" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;90s&quot;</span>,</span>
<span id="cb7-24"><a href="#cb7-24" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;millennial&quot;</span></span>
<span id="cb7-25"><a href="#cb7-25" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb7-26"><a href="#cb7-26" aria-hidden="true" tabindex="-1"></a>    <span class="at">edu_fct =</span> <span class="fu">c</span>(</span>
<span id="cb7-27"><a href="#cb7-27" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;(below) lower secondary&quot;</span>,</span>
<span id="cb7-28"><a href="#cb7-28" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;upper secondary&quot;</span>,</span>
<span id="cb7-29"><a href="#cb7-29" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;tertiary&quot;</span></span>
<span id="cb7-30"><a href="#cb7-30" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb7-31"><a href="#cb7-31" aria-hidden="true" tabindex="-1"></a>    <span class="at">grid_type =</span> <span class="st">&quot;counterfactual&quot;</span></span>
<span id="cb7-32"><a href="#cb7-32" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb7-33"><a href="#cb7-33" aria-hidden="true" tabindex="-1"></a>    <span class="at">re.form =</span> <span class="cn">NA</span></span>
<span id="cb7-34"><a href="#cb7-34" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb7-35"><a href="#cb7-35" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-36"><a href="#cb7-36" aria-hidden="true" tabindex="-1"></a>plot_pp_two_green <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(</span>
<span id="cb7-37"><a href="#cb7-37" aria-hidden="true" tabindex="-1"></a>  <span class="at">data =</span> pp_two_green,</span>
<span id="cb7-38"><a href="#cb7-38" aria-hidden="true" tabindex="-1"></a>  <span class="fu">aes</span>(</span>
<span id="cb7-39"><a href="#cb7-39" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> cohort_fct, <span class="at">y =</span> estimate, <span class="at">ymin =</span> conf.low,</span>
<span id="cb7-40"><a href="#cb7-40" aria-hidden="true" tabindex="-1"></a>    <span class="at">ymax =</span> conf.high, <span class="at">color =</span> edu_fct</span>
<span id="cb7-41"><a href="#cb7-41" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb7-42"><a href="#cb7-42" aria-hidden="true" tabindex="-1"></a>) <span class="sc">+</span></span>
<span id="cb7-43"><a href="#cb7-43" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept =</span> <span class="dv">0</span>, <span class="at">color =</span> <span class="st">&quot;grey70&quot;</span>) <span class="sc">+</span></span>
<span id="cb7-44"><a href="#cb7-44" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_pointrange</span>(<span class="at">position =</span> <span class="fu">position_dodge</span>(<span class="at">width =</span> <span class="fu">c</span>(<span class="fl">0.2</span>))) <span class="sc">+</span></span>
<span id="cb7-45"><a href="#cb7-45" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb7-46"><a href="#cb7-46" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> <span class="cn">NULL</span>,</span>
<span id="cb7-47"><a href="#cb7-47" aria-hidden="true" tabindex="-1"></a>    <span class="at">y =</span> <span class="st">&quot;Predicted probability&quot;</span>,</span>
<span id="cb7-48"><a href="#cb7-48" aria-hidden="true" tabindex="-1"></a>    <span class="at">color =</span> <span class="cn">NULL</span></span>
<span id="cb7-49"><a href="#cb7-49" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb7-50"><a href="#cb7-50" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_viridis_d</span>(<span class="at">option =</span> <span class="st">&quot;plasma&quot;</span>, <span class="at">end =</span> .<span class="dv">8</span>) <span class="sc">+</span></span>
<span id="cb7-51"><a href="#cb7-51" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_light</span>() <span class="sc">+</span></span>
<span id="cb7-52"><a href="#cb7-52" aria-hidden="true" tabindex="-1"></a>  <span class="fu">my_theme</span>() <span class="sc">+</span></span>
<span id="cb7-53"><a href="#cb7-53" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;top&quot;</span>)</span>
<span id="cb7-54"><a href="#cb7-54" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-55"><a href="#cb7-55" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(</span>
<span id="cb7-56"><a href="#cb7-56" aria-hidden="true" tabindex="-1"></a>  <span class="at">filename =</span> <span class="fu">here</span>(<span class="st">&quot;figs&quot;</span>, <span class="st">&quot;final&quot;</span>, <span class="st">&quot;fig_1_upper.png&quot;</span>),</span>
<span id="cb7-57"><a href="#cb7-57" aria-hidden="true" tabindex="-1"></a>  <span class="at">units =</span> <span class="fu">c</span>(<span class="st">&quot;in&quot;</span>),</span>
<span id="cb7-58"><a href="#cb7-58" aria-hidden="true" tabindex="-1"></a>  <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">5</span>,</span>
<span id="cb7-59"><a href="#cb7-59" aria-hidden="true" tabindex="-1"></a>  <span class="at">dpi =</span> <span class="dv">300</span></span>
<span id="cb7-60"><a href="#cb7-60" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb7-61"><a href="#cb7-61" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-62"><a href="#cb7-62" aria-hidden="true" tabindex="-1"></a><span class="co"># marginal effects</span></span>
<span id="cb7-63"><a href="#cb7-63" aria-hidden="true" tabindex="-1"></a>me_two_green <span class="ot">&lt;-</span> <span class="fu">avg_slopes</span>(</span>
<span id="cb7-64"><a href="#cb7-64" aria-hidden="true" tabindex="-1"></a>  two_green,</span>
<span id="cb7-65"><a href="#cb7-65" aria-hidden="true" tabindex="-1"></a>  <span class="at">variables =</span> <span class="st">&quot;edu_fct&quot;</span>,</span>
<span id="cb7-66"><a href="#cb7-66" aria-hidden="true" tabindex="-1"></a>  <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&quot;cohort_fct&quot;</span>),</span>
<span id="cb7-67"><a href="#cb7-67" aria-hidden="true" tabindex="-1"></a>  <span class="at">newdata =</span> <span class="fu">datagrid</span>(</span>
<span id="cb7-68"><a href="#cb7-68" aria-hidden="true" tabindex="-1"></a>    <span class="at">cohort_fct =</span> <span class="fu">c</span>(</span>
<span id="cb7-69"><a href="#cb7-69" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;interwar&quot;</span>,</span>
<span id="cb7-70"><a href="#cb7-70" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;post-war&quot;</span>,</span>
<span id="cb7-71"><a href="#cb7-71" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;60-70s&quot;</span>,</span>
<span id="cb7-72"><a href="#cb7-72" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;80s&quot;</span>,</span>
<span id="cb7-73"><a href="#cb7-73" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;90s&quot;</span>,</span>
<span id="cb7-74"><a href="#cb7-74" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;millennial&quot;</span></span>
<span id="cb7-75"><a href="#cb7-75" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb7-76"><a href="#cb7-76" aria-hidden="true" tabindex="-1"></a>    <span class="at">edu_fct =</span> <span class="fu">c</span>(</span>
<span id="cb7-77"><a href="#cb7-77" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;(below) lower secondary&quot;</span>,</span>
<span id="cb7-78"><a href="#cb7-78" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;upper secondary&quot;</span>,</span>
<span id="cb7-79"><a href="#cb7-79" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;tertiary&quot;</span></span>
<span id="cb7-80"><a href="#cb7-80" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb7-81"><a href="#cb7-81" aria-hidden="true" tabindex="-1"></a>    <span class="at">grid_type =</span> <span class="st">&quot;counterfactual&quot;</span></span>
<span id="cb7-82"><a href="#cb7-82" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb7-83"><a href="#cb7-83" aria-hidden="true" tabindex="-1"></a>  <span class="at">re.form=</span><span class="cn">NA</span></span>
<span id="cb7-84"><a href="#cb7-84" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb7-85"><a href="#cb7-85" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-86"><a href="#cb7-86" aria-hidden="true" tabindex="-1"></a>me_two_green <span class="ot">&lt;-</span> me_two_green <span class="sc">%&gt;%</span></span>
<span id="cb7-87"><a href="#cb7-87" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">contrast =</span> <span class="fu">case_when</span>(</span>
<span id="cb7-88"><a href="#cb7-88" aria-hidden="true" tabindex="-1"></a>    contrast <span class="sc">==</span> <span class="st">&quot;tertiary - (below) lower secondary&quot;</span> <span class="sc">~</span></span>
<span id="cb7-89"><a href="#cb7-89" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;high vs low edu&quot;</span>,</span>
<span id="cb7-90"><a href="#cb7-90" aria-hidden="true" tabindex="-1"></a>    contrast <span class="sc">==</span> <span class="st">&quot;upper secondary - (below) lower secondary&quot;</span> <span class="sc">~</span></span>
<span id="cb7-91"><a href="#cb7-91" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;medium vs low edu&quot;</span></span>
<span id="cb7-92"><a href="#cb7-92" aria-hidden="true" tabindex="-1"></a>  ))</span>
<span id="cb7-93"><a href="#cb7-93" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-94"><a href="#cb7-94" aria-hidden="true" tabindex="-1"></a>plot_me_two_green <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(<span class="at">data =</span> me_two_green, <span class="fu">aes</span>(</span>
<span id="cb7-95"><a href="#cb7-95" aria-hidden="true" tabindex="-1"></a>  <span class="at">x =</span> cohort_fct, <span class="at">y =</span> estimate,</span>
<span id="cb7-96"><a href="#cb7-96" aria-hidden="true" tabindex="-1"></a>  <span class="at">ymin =</span> conf.low, <span class="at">ymax =</span> conf.high,</span>
<span id="cb7-97"><a href="#cb7-97" aria-hidden="true" tabindex="-1"></a>  <span class="at">color =</span> contrast</span>
<span id="cb7-98"><a href="#cb7-98" aria-hidden="true" tabindex="-1"></a>)) <span class="sc">+</span></span>
<span id="cb7-99"><a href="#cb7-99" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept =</span> <span class="dv">0</span>, <span class="at">color =</span> <span class="st">&quot;grey70&quot;</span>) <span class="sc">+</span></span>
<span id="cb7-100"><a href="#cb7-100" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_pointrange</span>(<span class="at">position =</span> <span class="fu">position_dodge</span>(<span class="at">width =</span> <span class="fu">c</span>(<span class="fl">0.2</span>))) <span class="sc">+</span></span>
<span id="cb7-101"><a href="#cb7-101" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb7-102"><a href="#cb7-102" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> <span class="cn">NULL</span>, <span class="at">color =</span> <span class="st">&quot;Education&quot;</span>,</span>
<span id="cb7-103"><a href="#cb7-103" aria-hidden="true" tabindex="-1"></a>    <span class="at">y =</span> <span class="st">&quot;Marginal effect of education&quot;</span></span>
<span id="cb7-104"><a href="#cb7-104" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb7-105"><a href="#cb7-105" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;#fe9f6d&quot;</span>, <span class="st">&quot;#8c2981&quot;</span>)) <span class="sc">+</span></span>
<span id="cb7-106"><a href="#cb7-106" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_light</span>() <span class="sc">+</span></span>
<span id="cb7-107"><a href="#cb7-107" aria-hidden="true" tabindex="-1"></a>  <span class="fu">my_theme</span>() <span class="sc">+</span></span>
<span id="cb7-108"><a href="#cb7-108" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;top&quot;</span>)</span>
<span id="cb7-109"><a href="#cb7-109" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-110"><a href="#cb7-110" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(</span>
<span id="cb7-111"><a href="#cb7-111" aria-hidden="true" tabindex="-1"></a>  <span class="at">filename =</span> <span class="fu">here</span>(<span class="st">&quot;figs&quot;</span>, <span class="st">&quot;final&quot;</span>, <span class="st">&quot;fig_1_lower.png&quot;</span>),</span>
<span id="cb7-112"><a href="#cb7-112" aria-hidden="true" tabindex="-1"></a>  <span class="at">units =</span> <span class="fu">c</span>(<span class="st">&quot;in&quot;</span>),</span>
<span id="cb7-113"><a href="#cb7-113" aria-hidden="true" tabindex="-1"></a>  <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">5</span>,</span>
<span id="cb7-114"><a href="#cb7-114" aria-hidden="true" tabindex="-1"></a>  <span class="at">dpi =</span> <span class="dv">300</span></span>
<span id="cb7-115"><a href="#cb7-115" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb7-116"><a href="#cb7-116" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-117"><a href="#cb7-117" aria-hidden="true" tabindex="-1"></a><span class="co"># Combine the plots vertically</span></span>
<span id="cb7-118"><a href="#cb7-118" aria-hidden="true" tabindex="-1"></a>combined_plot <span class="ot">&lt;-</span> plot_pp_two_green <span class="sc">/</span> plot_me_two_green</span>
<span id="cb7-119"><a href="#cb7-119" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-120"><a href="#cb7-120" aria-hidden="true" tabindex="-1"></a><span class="co"># Save the combined plot</span></span>
<span id="cb7-121"><a href="#cb7-121" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(</span>
<span id="cb7-122"><a href="#cb7-122" aria-hidden="true" tabindex="-1"></a>  <span class="at">filename =</span> <span class="fu">here</span>(<span class="st">&quot;figs&quot;</span>, <span class="st">&quot;final&quot;</span>, <span class="st">&quot;figure_1.png&quot;</span>),</span>
<span id="cb7-123"><a href="#cb7-123" aria-hidden="true" tabindex="-1"></a>  <span class="at">plot =</span> combined_plot,</span>
<span id="cb7-124"><a href="#cb7-124" aria-hidden="true" tabindex="-1"></a>  <span class="at">units =</span> <span class="fu">c</span>(<span class="st">&quot;in&quot;</span>),</span>
<span id="cb7-125"><a href="#cb7-125" aria-hidden="true" tabindex="-1"></a>  <span class="at">width =</span> <span class="dv">8</span>,</span>
<span id="cb7-126"><a href="#cb7-126" aria-hidden="true" tabindex="-1"></a>  <span class="at">height =</span> <span class="dv">10</span>,</span>
<span id="cb7-127"><a href="#cb7-127" aria-hidden="true" tabindex="-1"></a>  <span class="at">dpi =</span> <span class="dv">300</span></span>
<span id="cb7-128"><a href="#cb7-128" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb7-129"><a href="#cb7-129" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb7-130"><a href="#cb7-130" aria-hidden="true" tabindex="-1"></a>combined_plot</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<div id="fig-fig_1" class="quarto-float quarto-figure quarto-figure-center anchored">
<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-fig_1-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<img role="img" 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" class="img-fluid figure-img" width="768">
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-fig_1-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure 5: Voting for a green party by cohort and education (twoway interaction)
</figcaption>
</figure>
</div>
</div>
</div>
</section>
<section id="voting-for-a-green-party-education-and-ess-wave" class="level2" data-number="3.2">
<h2 data-number="3.2" class="anchored" data-anchor-id="voting-for-a-green-party-education-and-ess-wave"><span class="header-section-number">3.2</span> Voting for a green party – education and ESS wave</h2>
<div class="cell">
<details class="code-fold">
<summary>Show the code</summary>
<div class="sourceCode cell-code" id="cb8"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="co"># predicted probabilities: by period</span></span>
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a>pp_two_green_period <span class="ot">&lt;-</span> <span class="fu">predictions</span>(</span>
<span id="cb8-3"><a href="#cb8-3" aria-hidden="true" tabindex="-1"></a>  two_green,</span>
<span id="cb8-4"><a href="#cb8-4" aria-hidden="true" tabindex="-1"></a>  <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&quot;essround&quot;</span>, <span class="st">&quot;edu_fct&quot;</span>),</span>
<span id="cb8-5"><a href="#cb8-5" aria-hidden="true" tabindex="-1"></a>  <span class="at">newdata =</span> <span class="fu">datagrid</span>(</span>
<span id="cb8-6"><a href="#cb8-6" aria-hidden="true" tabindex="-1"></a>    <span class="at">essround =</span> <span class="fu">c</span>(<span class="st">&quot;1&quot;</span>, <span class="st">&quot;2&quot;</span>, <span class="st">&quot;3&quot;</span>, <span class="st">&quot;4&quot;</span>, <span class="st">&quot;5&quot;</span>, <span class="st">&quot;6&quot;</span>, <span class="st">&quot;7&quot;</span>, <span class="st">&quot;8&quot;</span>, <span class="st">&quot;9&quot;</span>, <span class="st">&quot;10&quot;</span>),</span>
<span id="cb8-7"><a href="#cb8-7" aria-hidden="true" tabindex="-1"></a>    <span class="at">edu_fct =</span> <span class="fu">c</span>(</span>
<span id="cb8-8"><a href="#cb8-8" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;(below) lower secondary&quot;</span>,</span>
<span id="cb8-9"><a href="#cb8-9" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;upper secondary&quot;</span>,</span>
<span id="cb8-10"><a href="#cb8-10" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;tertiary&quot;</span></span>
<span id="cb8-11"><a href="#cb8-11" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb8-12"><a href="#cb8-12" aria-hidden="true" tabindex="-1"></a>    <span class="at">grid_type =</span> <span class="st">&quot;counterfactual&quot;</span></span>
<span id="cb8-13"><a href="#cb8-13" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb8-14"><a href="#cb8-14" aria-hidden="true" tabindex="-1"></a>    <span class="at">re.form =</span> <span class="cn">NA</span></span>
<span id="cb8-15"><a href="#cb8-15" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb8-16"><a href="#cb8-16" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-17"><a href="#cb8-17" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(</span>
<span id="cb8-18"><a href="#cb8-18" aria-hidden="true" tabindex="-1"></a>  <span class="at">data =</span> pp_two_green_period,</span>
<span id="cb8-19"><a href="#cb8-19" aria-hidden="true" tabindex="-1"></a>  <span class="fu">aes</span>(</span>
<span id="cb8-20"><a href="#cb8-20" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> essround, <span class="at">y =</span> estimate, <span class="at">ymin =</span> conf.low,</span>
<span id="cb8-21"><a href="#cb8-21" aria-hidden="true" tabindex="-1"></a>    <span class="at">ymax =</span> conf.high, <span class="at">color =</span> edu_fct</span>
<span id="cb8-22"><a href="#cb8-22" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb8-23"><a href="#cb8-23" aria-hidden="true" tabindex="-1"></a>) <span class="sc">+</span></span>
<span id="cb8-24"><a href="#cb8-24" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_pointrange</span>(<span class="at">position =</span> <span class="fu">position_dodge</span>(<span class="at">width =</span> <span class="fu">c</span>(<span class="fl">0.2</span>))) <span class="sc">+</span></span>
<span id="cb8-25"><a href="#cb8-25" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb8-26"><a href="#cb8-26" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> <span class="cn">NULL</span>, <span class="at">y =</span> <span class="st">&quot;Predicted probability&quot;</span>,</span>
<span id="cb8-27"><a href="#cb8-27" aria-hidden="true" tabindex="-1"></a>    <span class="at">color =</span> <span class="cn">NULL</span></span>
<span id="cb8-28"><a href="#cb8-28" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb8-29"><a href="#cb8-29" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_viridis_d</span>(<span class="at">option =</span> <span class="st">&quot;plasma&quot;</span>, <span class="at">end =</span> .<span class="dv">8</span>) <span class="sc">+</span></span>
<span id="cb8-30"><a href="#cb8-30" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_light</span>() <span class="sc">+</span></span>
<span id="cb8-31"><a href="#cb8-31" aria-hidden="true" tabindex="-1"></a>  <span class="fu">my_theme</span>() <span class="sc">+</span></span>
<span id="cb8-32"><a href="#cb8-32" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;top&quot;</span>)</span>
<span id="cb8-33"><a href="#cb8-33" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb8-34"><a href="#cb8-34" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(</span>
<span id="cb8-35"><a href="#cb8-35" aria-hidden="true" tabindex="-1"></a>  <span class="at">filename =</span> <span class="fu">here</span>(<span class="st">&quot;figs&quot;</span>, <span class="st">&quot;final&quot;</span>, <span class="st">&quot;appendix_fig_d1.png&quot;</span>),</span>
<span id="cb8-36"><a href="#cb8-36" aria-hidden="true" tabindex="-1"></a>  <span class="at">units =</span> <span class="fu">c</span>(<span class="st">&quot;in&quot;</span>),</span>
<span id="cb8-37"><a href="#cb8-37" aria-hidden="true" tabindex="-1"></a>  <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">5</span>,</span>
<span id="cb8-38"><a href="#cb8-38" aria-hidden="true" tabindex="-1"></a>  <span class="at">dpi =</span> <span class="dv">300</span></span>
<span id="cb8-39"><a href="#cb8-39" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<div id="fig-fig_d1" class="quarto-float quarto-figure quarto-figure-center anchored">
<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-fig_d1-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<img role="img" 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" class="img-fluid figure-img" width="768">
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-fig_d1-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure 6: Predicted probabilities of voting for a green party by education and ESS wave (Appendix)
</figcaption>
</figure>
</div>
</div>
</div>
</section>
<section id="voting-for-a-green-party-education-and-age-group" class="level2" data-number="3.3">
<h2 data-number="3.3" class="anchored" data-anchor-id="voting-for-a-green-party-education-and-age-group"><span class="header-section-number">3.3</span> Voting for a green party – education and age group</h2>
<div class="cell">
<details class="code-fold">
<summary>Show the code</summary>
<div class="sourceCode cell-code" id="cb9"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="co"># predicted probabilities: by age group</span></span>
<span id="cb9-2"><a href="#cb9-2" aria-hidden="true" tabindex="-1"></a>pp_two_green_age <span class="ot">&lt;-</span> <span class="fu">predictions</span>(</span>
<span id="cb9-3"><a href="#cb9-3" aria-hidden="true" tabindex="-1"></a>  two_green,</span>
<span id="cb9-4"><a href="#cb9-4" aria-hidden="true" tabindex="-1"></a>  <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&quot;age_grp_fct&quot;</span>, <span class="st">&quot;edu_fct&quot;</span>),</span>
<span id="cb9-5"><a href="#cb9-5" aria-hidden="true" tabindex="-1"></a>  <span class="at">newdata =</span> <span class="fu">datagrid</span>(</span>
<span id="cb9-6"><a href="#cb9-6" aria-hidden="true" tabindex="-1"></a>    <span class="at">age_grp_fct =</span> <span class="fu">c</span>(<span class="st">&quot;22-29&quot;</span>, <span class="st">&quot;30-65&quot;</span>, <span class="st">&quot;above 65&quot;</span>),</span>
<span id="cb9-7"><a href="#cb9-7" aria-hidden="true" tabindex="-1"></a>    <span class="at">edu_fct =</span> <span class="fu">c</span>(</span>
<span id="cb9-8"><a href="#cb9-8" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;(below) lower secondary&quot;</span>,</span>
<span id="cb9-9"><a href="#cb9-9" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;upper secondary&quot;</span>,</span>
<span id="cb9-10"><a href="#cb9-10" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;tertiary&quot;</span></span>
<span id="cb9-11"><a href="#cb9-11" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb9-12"><a href="#cb9-12" aria-hidden="true" tabindex="-1"></a>    <span class="at">grid_type =</span> <span class="st">&quot;counterfactual&quot;</span></span>
<span id="cb9-13"><a href="#cb9-13" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb9-14"><a href="#cb9-14" aria-hidden="true" tabindex="-1"></a>    <span class="at">re.form =</span> <span class="cn">NA</span></span>
<span id="cb9-15"><a href="#cb9-15" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb9-16"><a href="#cb9-16" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-17"><a href="#cb9-17" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(</span>
<span id="cb9-18"><a href="#cb9-18" aria-hidden="true" tabindex="-1"></a>  <span class="at">data =</span> pp_two_green_age,</span>
<span id="cb9-19"><a href="#cb9-19" aria-hidden="true" tabindex="-1"></a>  <span class="fu">aes</span>(</span>
<span id="cb9-20"><a href="#cb9-20" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> age_grp_fct, <span class="at">y =</span> estimate, <span class="at">ymin =</span> conf.low,</span>
<span id="cb9-21"><a href="#cb9-21" aria-hidden="true" tabindex="-1"></a>    <span class="at">ymax =</span> conf.high, <span class="at">color =</span> edu_fct</span>
<span id="cb9-22"><a href="#cb9-22" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb9-23"><a href="#cb9-23" aria-hidden="true" tabindex="-1"></a>) <span class="sc">+</span></span>
<span id="cb9-24"><a href="#cb9-24" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_pointrange</span>(<span class="at">position =</span> <span class="fu">position_dodge</span>(<span class="at">width =</span> <span class="fu">c</span>(<span class="fl">0.2</span>))) <span class="sc">+</span></span>
<span id="cb9-25"><a href="#cb9-25" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb9-26"><a href="#cb9-26" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> <span class="cn">NULL</span>, <span class="at">y =</span> <span class="st">&quot;Predicted probability&quot;</span>,</span>
<span id="cb9-27"><a href="#cb9-27" aria-hidden="true" tabindex="-1"></a>    <span class="at">color =</span> <span class="cn">NULL</span></span>
<span id="cb9-28"><a href="#cb9-28" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb9-29"><a href="#cb9-29" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_viridis_d</span>(<span class="at">option =</span> <span class="st">&quot;plasma&quot;</span>, <span class="at">end =</span> .<span class="dv">8</span>) <span class="sc">+</span></span>
<span id="cb9-30"><a href="#cb9-30" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_light</span>() <span class="sc">+</span></span>
<span id="cb9-31"><a href="#cb9-31" aria-hidden="true" tabindex="-1"></a>  <span class="fu">my_theme</span>() <span class="sc">+</span></span>
<span id="cb9-32"><a href="#cb9-32" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;top&quot;</span>)</span>
<span id="cb9-33"><a href="#cb9-33" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb9-34"><a href="#cb9-34" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(</span>
<span id="cb9-35"><a href="#cb9-35" aria-hidden="true" tabindex="-1"></a>  <span class="at">filename =</span> <span class="fu">here</span>(<span class="st">&quot;figs&quot;</span>, <span class="st">&quot;final&quot;</span>, <span class="st">&quot;appendix_fig_d2.png&quot;</span>),</span>
<span id="cb9-36"><a href="#cb9-36" aria-hidden="true" tabindex="-1"></a>  <span class="at">units =</span> <span class="fu">c</span>(<span class="st">&quot;in&quot;</span>),</span>
<span id="cb9-37"><a href="#cb9-37" aria-hidden="true" tabindex="-1"></a>  <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">5</span>,</span>
<span id="cb9-38"><a href="#cb9-38" aria-hidden="true" tabindex="-1"></a>  <span class="at">dpi =</span> <span class="dv">300</span></span>
<span id="cb9-39"><a href="#cb9-39" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<div id="fig-fig_d2" class="quarto-float quarto-figure quarto-figure-center anchored">
<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-fig_d2-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<img role="img" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABgAAAAPACAMAAADE4EJKAAAA9lBMVEUAAAAAADoAAGYAOjoAOmYAOpAAZrYNCIc6AAA6ADo6OgA6Ojo6OmY6Zjo6ZmY6ZpA6ZrY6kLY6kNtmAABmADpmOgBmOjpmOmZmZgBmZjpmZmZmZpBmkJBmkLZmkNtmtttmtv+QOgCQZjqQZmaQkGaQkLaQkNuQtpCQttuQ27aQ29uQ2/+xKpCzs7O2ZgC2Zjq2kDq2kGa2kJC2kLa2tma2tpC2tra2ttu225C229u22/+2/7a2///bkDrbkGbbtmbbtpDbtrbb25Db27bb29vb2//b/7bb/9vb///e3t78pjb/tmb/25D/27b/29v//7b//9v///+0hVPwAAAACXBIWXMAAB2HAAAdhwGP5fFlAAAgAElEQVR4nO3dDX/bZpaecSi2TNbyxo68dpvJuH7Z6W625tBJ2mZqN4o8TdJ6RNkiv/+XWeAABME38SEFPDz3g+v/a3ckS6boHOhcEl/AbAYA6KXs2FcAAHAcBAAAeooAAEBPEQAA6CkCAAA9RQAAoKcIAAD0FAEAgJ4iAADQUwQAAHqKAABATxEAAOgpAgAAPUUAAKCnCAAA9BQBAICeIgAA0FMEAAB6igAAQE8RAADoKQIAAD1FAACgpwgAAPQUAQCAniIAANBTBAAAeooAAEBPEQAA6CkCAAA9RQAAoKcIAGaDwbGvgYTR6NjXoENXV8e+BjgGAoDBgAIEGI0SLsDVFQXoJQLQd4O5rZ8xPv00G2evNn7s5ulXv9x68dfDxl/c+dldm2QPDvybo7lWr0+rDv/HXc1t/vD0xyfb/mb5oYvs/MCvjCMjAD03GOwqwKTY/QcHYDZufIJuAEYj/wU4+B93dXV7AW654PJDBEAWAei3wWBXAW6eFt/ihweg/Puhn92xQ3fkaCRQgEP/cVdXOwqwMwCQRQB6bTDYWYCLk7ezuwSguoDQz+7WgftqNFIowIH/uKurXQUgAOkiAL22OwA3T08/zSwAHx9mJy8+2R9+fjPMskcfZvVK//wmy7LHH4r3bd2XtwlcD4u/ez18sLiwnZ+9uOh8uZxfDrP783x8fp7/rbOXs5VrkPv4LMvu1R+oLtuu8+XD+Tuz/KJOXlb76mNxUffsH1N9kb9k9s/Mr8qmhbY7AOPyRhD76/nXvX5W/bdqvu30H7crANPX+eXZ31y7+vey8kPVTUD7fmUcHwHos8Gq9U+pvrfH2Z+y+R7IV7W9ffJqvtIniz8of1UYVzcNF/93+rr+FWD3ZzcuOl8iX+fvlRuk/sjaNSj+orHr2bjs/GLP7B2Lztje/JP99XfZ4qKqL/L/yjTly27DbzqjVeufshyAPw/nF9982+c/7mrV6ifUAVi/+vefNQOw71eGAwSgz3YHYL698xVz+qH4QdOWfvbk02z6Q7F8bKXnm+HxH7Ppuyz/3EmxC/LPKL7xq9uNFncR7vzs5kXnH6wXpO2Qb/J3Pg6L67P0afnl5R+5nL9dX3ZxnR98qq7zJP8JeTYd23bK3/4+v8RL+6T5Fymv6+bbqPYOwOK/1bb/bn7+cTsDML+dZ+PVb9wJvPdXhgMEoM92B2D+rTsuf9S8KL6t57/PF9/29vH5HxQ/ydsNPtfDs3wLzP/u4nbinZ/dvOhio7xtXJHGO0ufVr1jq6Z52fn/qXbQ+XxB54v2Qb2syx9L519kUv3gGnAL0O4ALP5bbfvv5ucfFxyAjVe/EYC9vzIcIAB9tjsAk+rn1Oq7u9jS9U06xY325R9Uv+AXnzx9bXv8z8NX1Y36xefNf/7b9dlLF11/bZP/tfvv67cbn9a8eWHpsqtblmxxzTdsfVv0l7//5VlWbqryi5S12nxf994BqP9bbf3v5ucfFxqAzVd/5WGge31lOEAAem3nXQCT+qdO+xYutkB5m3B1A3TxDV7//FquvPyd8Vf/5+l5vRYWNwDs+uyli17ekeWN4ff/26fZbLZyDZqPMlq67MVSXv5l5POz6m83NlV1C9Tmmyr2vhN4/t9q6383R/+4wEcBbb76zQDs/ZVxfASg1wLuA14LwM3TtQBU39/2xiR7dfP09P+/flD/yLjYYrs+e+miV284KB70knvyabZ2DeafsnzZjR05/yVkMr8z8+TRn99Xt1VUX6S4O2LbTRWtBMDrPy4wAJuvfiMA+39lHB8B6LXDArB06/Xqz/TXw/P8/83G80eArAdg+2cvXfT63pj+VDzO8Hy2cg32/CG5uK28+lG7uamKx7tuu6mipQD4/McFB2DT1V8E4ICvjOMjAP2262kAi5uAbN8UP2wuPaZv7Vb9fAGcXubvT776n4sdsPU+gJXPXn644KYfHKc/ZMu3jM9uu5m8sSMXN5PP385/pG1uqtn45L+/3nZTxc7ngW24D8B+MN/2383TP27X/q9vAtpw9RcBOOQr4+gIQM/dvv/rO3KrB53Ygruob/8t7wRefnjKbPzVf8lXwfV/+na+MJbvBL71s5sXvbwj5xdiV2jp0y4Wd4euPlBmdSnbA2Xmm2qSlQ9nmX+R4qHrW2+q2LH/66c8NB6gc7H6ttt/3I79X2/5DVd/PQB7fWUcGwHou1v3f+NhoPaw86x6PPvph1n5GPTGI/u/vCkf+lc+BDz/pPlNBisPA73ts5sXvbwj8/VWfOTz6+r26MWnlQ+V/zh/e3HZzR2Zf+C8eAh9fStJ/tP2yqYqbuTefk6z2/d/8c94WVy5ch8vHqLffNvvP+72/V/do7vx6pcfWtwEtO9XxpERAGzd/rPmE8HKZwLbeYGrZ6QW76w9t9fWUXmLwfxhLitPBLv1sxsXvXIryfzJsvd/Wfm0+ZNlHzT+fvVk2cWOrD7Jniw7KT//9IfynfkXya9D81buNdu3/2z+GJnyhqz5f6vz2fLbnv9x27d/de2Kn/o3XP3yQ9VTAw76yjgqAoBbTeangnhl55sp/7A8KUzxvM/Vs/vM7Du+XH3V2l87FcStn7246NWbye0j95bOqvN9+ZGtp8tp7MjicTbz0+VUn796U8xFnawDTPOve//DpLoTuDhPzsvFf7d7a//dtP5xvw6zxXmalq++fahMfBdfGR0jALhVdTK4O7hWuQm4rbPaNx/04uYBMMc7ZT8vFuAZAcDtLu66wsYitwA0flO5G48BaO0fJ/SVEYAA4HZ3/RWg+YIwrl229XQljwFo7R8n9JURgABgh8nddthY40Hg46y1+yr9BaDFf5zMV0YQAoBdxnf5FWDpReEd+6G+h/vO/AWgxX+czFdGEAIAAD1FAACgpwgAAPQUAQCAniIAANBTBAAAeooAAEBPEQAA6CkCAAA9RQAAoKcIAAD0FAEAgJ4iAADQUwQAAHqKAABATxEAAOgpAgAAPUUAAKCnCAAA9BQBAICeIgAA0FMEAAB6igAAQE8RAADoKQIAAD1FAACgpwgAAPQUAQCAniIAANBTBAAAeooAAEBPEQAA6CkCAAA9RQAAoKcIAAD0FAEAgJ4iAADQUwQAAHqKAABATxEAAOgpAgAAPXWEAPwVAHAUxw9A/C/p0T+OfQVwEOamibkZAuAFB6Qm5qaJuRkC4AUHpCbmpom5GQLgBQekJuamibkZAuAFB6Qm5qaJuRkC4AUHpCbmpom5GQLgBQekJuamibkZAuAFB6Qm5qaJuRkC4AUHpCbmpom5GQLgBQekJuamibkZAuAFB6Qm5qaJuRkC4AUHpCbmpom5GQLgBQekJuamibkZAuAFB6Qm5qaJuRkC4AUHpCbmpom5GQLgBQekJuamibkZAuAFB6Qm5qaJuRkC4AUHpCbmpom5GQLgBQekJuamibkZAuAFB6Qm5qaJuRkC4AUHpCbmpom5GQLgBQekJuamibkZAuAFB6Qm5qaJuRkC4AUHpCbmpom5GQLgBQekJuamibkZAuAFB6Qm5qaJuRkC4AUHpCbmpom5GQLgBQekJuamibkZAuAFB6Qm5qaJuRkC4AUHpCbmpom5GQLgBQekJuamibkZAuAFB6Qm5qaJuRkC4AUHpCbmpom5GQLgBQekJuamibkZAuAFB6Qm5qaJuRkC4MWtB+TVVayrgT2xSDQxN0MAvCAAmlgkmpibIQBeEABNLBJNzM0QAC8IgCYWiSbmZgiAFwRAE4tEE3MzBMALAqCJRaKJuRkC4AUB0MQi0cTcDAHwggBoYpFoYm6GAHhBADSxSDQxN0MAvCAAmlgkmpibIQBeEABNLBJNzM0QAC8IgCYWiSbmZgiAFwRAE4tEE3MzBMALAqCJRaKJuRkC4AUB0MQi0cTcDAHwggBoYpFoYm6GAHhBADSxSDQxN0MAvCAAmlgkmpibIQBeEABNLBJNzM0QAC8IgCYWiSbmZgiAFwRAE4tEE3MzBMALAqCJRaKJuRkC4AUB0MQi0cTcDAHwggBoYpFoYm6GAHhBADSxSDQxN0MAvCAAmlgkmpibIQBeEABNLBJNzM0QAC8IgCYWiSbmZgiAFwRAE4tEE3MzBMALAqCJRaKJuRkC4AUB0MQi0cTcDAHwggBoYpFoYm6GAHhBADSxSDQxN0MAvCAAmlgkmpibIQBeEABNLBJNzM0QAC8IgCYWiSbmZgiAFwRAE4tEE3MzBMALAqCJRaKJuRkC4AUB0MQi0cTcDAHwggBoYpFoYm6GAHhBADSxSDQxN0MAvCAAmlgkmpibIQBeEABNLBJNzM0QAC8IgCYWiSbmZgiAFwRAE4tEE3MzBMALAqCJRaKJuRkC4AUB0MQi0cTcDAHwggBoYpFoYm6GAHhBADSxSDQxN0MAvCAAmlgkmpibIQBeEABNLBJNzM0QAC8IgCYWiSbmZgiAFwRAE4tEE3MzBMALAqCJRaKJuRkC4AUB0MQi0cTcDAHwggBoYpFoYm6GAHhBADSxSDQxN0MAvCAAmlgkmpibIQBeEABNLBJNzM0QAC8IgCYWiSbmZgiAFwRAE4tEE3MzBMALAqCJRaKJuRkC4AUB0MQi0cTcDAHwggBoYpFoYm6GAHhBADSxSDQxN0MAvCAAmpibJgJgCIAXLBJNzE0TATAEwAsWiSbmpokAGALgBYtEE3PTRAAMAfCCRaKJuWkiAIYAeMEi0cTcNBEAQwC8YJFoYm6aCIAhAF6wSDQxN00EwBAAL1gkmpibJgJgCIAXLBJNzE0TATAEwAsWiSbmpokAGALgBYtEE3PTRAAMAfCCRaKJuWkiAIYAeMEi0cTcNBEAQwC8YJFoYm6aCIAhAF6wSDQxN00EwBAAL1gkmpibJgJgCIAXLBJNzE0TATAEwAsWiSbmpokAGALgBYtEE3PTRAAMAfCCRaKJuWkiAIYAeMEi0cTcNBEAQwC8YJFoYm6aCIAhAF6wSDQxN00EwBAAL1gkmpibJgJgCIAXLBJNzE0TATAEwAsWiSbmpokAGALgxW0H5NUVm8QrAqCJABgC4MX2A/JqLuK1QSgCoIkAGALgxdYD8uqKAjhGADQRAEMAvNh2QF5dUQDPCIAmAmAIgBdbDsirKwrgGgHQRAAMAfCCAGgiAJoIgCEAXmw+IK9WRb5W2IUAaCIAhgB4QQA0EQBNBMAQAC8IgCYCoIkAGALgBQHQRAA0EQBDALzgTmBNBEATATAEwAsCoIkAaCIAhgB4QQA0EQBNBMAQAC94JrAmAqCJABgC4AXnAtJEADQRAEMAvOBsoJoIgCYCYAiAF7wegCYCoIkAGALgBYtEE3PTRAAMAfCCRaKJuWkiAIYAeMEi0cTcNBEAQwC8YJFoYm6aCIAhAF6wSDQxN00EwBAAL1gkmpibJgJgCIAXLBJNzE0TATAEwAsWiSbmpokAGALgBYtEE3PTRAAMAfCCRaKJuWkiAIYAeMEi0cTcNBEAQwC8YJFoYm6aCIAhAF6wSDQxN00EwBAAL1gkmpibJgJgCIAXLBJNzE0TATAEwAsWiSbmpokAGALgBYtEE3PTRAAMAfCCRaKJV3LTRAAMAfCCAGjitZw1EQBDALwgAJq2zu3qigI4RgAMAfCCAGjaNrerKwrgGQEwBMALAqBpy9yuriiAawTAEAAvCIAmAqCJABgC4AUB0LR5blerIl8r7EIADAHwggBoIgCaCIAhAF4QAE0EQBMBMATACwKgiQBoIgCGAHhBADRxJ7AmAmAIgBcEQBMB0EQADAHwggBoIgCaCIAhAF4QAE08E1gTATAEwAsCoIlzAWkiAIYAeEEANHE2UE0EwBAALwiAJl4PQBMBMATACwKgiblpIgCGAHjBItHE3DQRAEMAvGCRaGJumgiAIQBesEg0MTdNBMAQAC9YJJqYmyYCYAiAFywSTcxNEwEwBMALFokm5qaJABgC4AWLRBNz00QADAHwgkWiiblpIgCGAHjBItHE3DQRAEMAvGCRaGJumgiAIQBesEg0MTdNBMAQAC9YJJqYmyYCYAiAFywSTcxNEwEwBMALFokm5qaJABgC4AWLRBNz00QADAHwgkWiiblpIgCGAHjBItHE3DQRAEMAvGCRaGJumgiAIQBesEg0MTdNBMAQAC9YJJqYmyYCYAiAFywSTcxNEwEwBMALFokm5qaJABgC4AWLRBNz00QADAHwgkWiiblpIgCGAHjBItHE3DQRAEMAvGCRaGJumgiAIQBesEg0MTdNBMAQAC9YJJqYmyYCYAiAFywSTcxNEwEwBMALFokm5qaJABgC4AWLRBNz00QADAHwgkWiiblpIgCGAHjBItHE3DQRAEMAvGCRaGJumgiAIQBesEg0MTdNBMAQAC9YJJqYmyYCYAiAFywSTcxNEwEwBMALFokm5qaJABgC4AWLRBNz00QADAHwgkWiiblpIgCGAHjBItHE3DQRAEMAvGCRaGJumgiAIQBesEg0MTdNBMAQAC9YJJqYmyYCYAiAFywSTcxNEwEwBMALFokm5qaJABgC4AWLRBNz00QADAHwgkWiiblpIgCGAHjBItHE3DQRAEMAvGCRaGJumgiAIQBesEg0MTdNBMAQAC9YJJqYm6bb5tajsREAL1gkmpibJgJgCIAXLBJNzE0TATAEwAsWiSbmpokAGALgBYtEE3PTRAAMAfCCRyVoIgCaCIAhAF4QAE0EQBMBMATACwKgiQBoIgCGAHhBADQRAE0EwBAALwiAJuamiQAYAuAFi0QTc9NEAAwB8IJFoom5aSIAhgB4wSLRxNw0EQBDALxgkWhibpoIgCEAXrBINDE3TQTAEAAvWCSamJsmAmAIgBcsEk3MTRMBMATACxaJJuamiQCYNgLw+XmWZY8+bPjI9PVXv+z8LAJgWCSamJsmAmBaCMAkMyev1j90kdUB2P5ZBMCwSDQxN00EwNw9ANfD7PTD7PPr7OTt6ocusjoAt3wWATAsEk3MTRMBMHcPwDg7/TQrbu3JHix/YPouWwRg62cRgAqLRBNz00QAzJ0DcPM0K2/VmSxu7jEfn2XZ2fzPtn7WjABUWCSamJsmAmDuHIDJ2o6fzd89eVl/cNtn2VXY90umiUWiiblpIgDmzgG4KG/bsVt3zht/fvP0yafF3t/2WXYV9v2SaWKRaGJumgiAuXMAxo3VvnTz/h+zxg/+Wz9rRgAqLBJNzE0TATAtBODB2lu1RgDWP+uvc/8AADeuro59DaI5YgDm+A3A8JOkJuamid8ADAHwgkWiiblpIgCGAHjBItHE3DQRANPdncAF7gQOxyLRxNw0EQDT2cNADQ8DDcci0cTcNBEA09kTwZY/yBPBdmKRaGJumgiA6e5UEEt/xqkgdmKRaGJumgiA6fBkcLPmuudkcLuwSDQxN00EwHR6OuhGADgd9C4sEk3MTRMBMC28IMzl0ku9TLLGjfyNG3wueUGY27FINDE3TQTAhAXgj1svY+nFHrcFgJeE3IFFoom5aSIAJigAN0/vvfjU3VXo7JKlsEg0MTdNBMAEBqD44f37rq5CR5crhkWiiblpIgAmPAC5xxtuwGnhKnRxoXpYJJqYmyYCYIICMP1xmFX34b64/e6Ag65C65coiUWiiblpIgAm9FFAX+oG3HvZ8t0BBMCwSDQxN00EwOzxMNAvPz6sGtDu3QEEwLBINDE3TQTA7Pc8gOm8AffXn/R1+FVo76KUsUg0MTdNBMDsFYB6/2ebnvZ78FVo7ZKksUg0MTdNBMCEB2Cx/U+KuwPWT/xz8FVo7ZKksUg0MTdNBMAEBmBx+//J4w+z6TjbcOrPg69CWxekjUWiiblpIgBmr+cBZNmj9/YH10MC0DYWiSbmpokAmH0CcP/7T4s/IAAtY5FoYm6aCIAJDsDS2YBunn/d3smBCIBhkWhibpoIgAkLwD938ATgxVXo7qKVsEg0MTdNBMCEnQriX/703fwH/uvnD5+0fBXavThVLBJNzE0TATChNwHVN/nnb5+2ey4IAmBYJJqYmyYCYPYOQKsPACqvQquXJotFoom5aSIAZmcAxtkaAtAFFokm5qaJAJidAbgergWAm4C6wCLRxNw0EQCz+yag9V8B1l/X/W5Xod2LU8Ui0cTcNBEAszsA18/PitNA3DsrPfqu7ZcFIwCGRaKJuWkiAGbvO4E7uAqdXbIUFokm5qaJAJi9nwfQwVXo7JKlsEg0MTdNBMDs94Iw3VyF+F/SIxaJJuamiQAYAuAFi0QTc9NEAMyuANw8P3v0S/5/mh7xPIAOsEg0MTdNBMDsDEBx/+/i9QB4IlhnWCSamJsmAmAIgBcsEk3MTRMBMATACxaJJuamiQAY7gT2gkWiiblpIgCGAHjBItHE3DQRAEMAvGCRaGJumgiAIQBesEg0MTdNBMAEPA9gDc8D6AKLRBNz00QATMCjgHhBmChYJJqYmyYCYAiAFywSTcxNEwEwBMALFokm5qaJABjuBPaCRaKJuWkiAIYAeMEi0cTcNBEAQwC8YJFoYm6abpnb1VV/CkAAvGCRaGJumrbO7Wou5rU5Gl4PwAsWiSbmpmnb3K6uelUAzgbqBYtEE3PTtGVuV1f9KgAB8IJFoom5ado8t6urnhWAAHjBItHE3DQRAMOdwF6wSDQxN00b53a1Kva1io4AeMEi0cTcNBEAQwC8YJFoYm6aCIAJD8CXn3N/+9TBVWj/IhWxSDQxN00EwIQG4OOz6h7g+x9avwptX6AmFokm5qaJO4FNYAAuGg8Cetn2VWj58kSxSDQxN00EwIQFYNJ8FOjJ25avQrsXp4pFoom5aSIAJiwA43zv33vx/vfffipuCXrQ8lVo9+JUsUg0MTdNPBPYBAWgeCLYfOuPeSJYN1gkmpibJs4FZEIDUN/uY08NbvcqtHppslgkmpibJs4GaoICMH29WPp5AE7bfSwoATAsEk3MTROvB2DC7gO4yLJX1ZsT7gPoBotEE3PTxCuCmbAA5L8CVLcBXQ7bvgWIAJRYJJqYmyYCYAJeEKbwsHj456Nvz4r/edHy04EJgGGRaGJumgiACTgd9BruBO4Ci0QTc9NEAAwB8IJFoom5aSIAhgB4wSLRxNw0EQDD6aC9YJFoYm6aCIAhAF6wSDQxN00EwBAAL1gkmpibJgJg9g/A9ON/5j6ADrBINDE3TQTABAbg83PuBO4Yi0QTc9NEAExYAJYfC0QAusAi0cTcNBEAE3wuIALQMRaJJuamiQCY0LOBZtm9h/n/P+MlIbvCItHE3DQRABP8gjD52h+fvF06M3RbV6Hdi1PFItHE3DQRABMagGLrX2Tns9n1kNNBd4JFoom5aSIAZp8ATIrVzwvCdIRFoom5aSIAJvg+APvhP8/AhDuBu8Ei0cTcNBEAE/4ooK9+yTtw79shAegGi0QTc9NEAEz48wDytV89GpT7ALrAItHE3DQRABP4TODpmyIA5fPBqheHbO8qtHtxqlgkmpibJgJggs8F9OV/fJrNPj/Lsvst738CUGKRaGJumgiA2ftkcO0+AsiuQuuXKIlFoom5aSIAhtNBe8Ei0cTcNBEAEx6ALz/n/tb+LwAEoMQi0cTcNBEAExqAj8+qM8Hd/9D6VWj7AjWxSDQxN00EwAQGoHk6UE4G1wkWiSbmpokAmLAATJpng+ZhoJ1gkWhibpoIgAkLwLg4HfSL97//9tMzngjWERaJJuamiQCY4NNBz7f+mFNBdINFoom5aSIAJjQA9e0+1ZlB27wKrV6aLBaJJuamiQCY0LOB1kuf00F3hEWiiblpIgAm+Gygr6o3J9wH0A0WiSbmpokAmLAA5L8CVLcBXQ55SchusEg0MTdNBMDsCsDN87PCw+Lhn4++LV4T/uRFy08HJgCGRaKJuWkiAGZnAJ5ma7gTuAssEk3MTRMBMATACxaJJuamiQAYAuAFi0QTc9NEAAyng/aCRaKJuWkiAIYAeMEi0cTcNBEAQwC8YJFoYm6aCIDZIwC//fzzz++7uAodXKYgFokm5qaJAJjQAEx/HJbngv6v7V+F1i9REotEE3PTRABMYACmr+vHALV8JiACUGGRaGJumgiACQzAuPEo0JZPBUQASiwSTcxNEwEw4a8IdvLi/e9//0txQ9CrjZ9z+FVo9+JUsUg0MTdNBMAEvyJYdctPcVsQZwPtAotEE3PTRABM6OsB1C8Icz3k9QA6wSLRxNw0EQAT+opgzReE4VQQXWCRaGJumgiAIQBesEg0MTdNBMCE3gSUnVdvX7T+QFACYFgkmpibJgJggl8SMvumWPvTH7JFC9q6Cu1enKjR6NjXAAchAJoIgAkLQHlS6Hv2wmC8JGQnCIAoAqCJAJjAJ4JNGk8Ea/lpAATAEABRBEATATCh5wK6HFbr/+Rl61eh7QuURABEEQBNBMAEnw10+mNx+8/Zi7bPBEQASgRAFAHQRAAMrwfgBAEQRQA0EQATFoBfO/jBf3EVurtoIQRAFAHQRABM6PMAWj8JdOMqdHbJSgiAKAKgiQCYvZ8J3MFV6OySlRAAUQRAEwEwe58MroOr0NklKyEAogiAJgJggl8P4P6Hzq5CVxcshQCIIgCaCIAJ+w3g72/KZwKXHnEyuPYRAFEEQBMBMKH3ATRxNtAOEABRBEATATAEwAkCIIoAaCIAhgA4QQBEEQBNBMDwTGAnCIAoAqCJABgC4AQBEEUANBEAQwCcIACiCIAmAmDCAzD9+88///y3Ds4IQQAKBEAUAdBEAExoAD4/q+4Bbv8JYQSgQABEEQBNBMAEBuBy8RigE14RrAsEQBQB0EQATFgAroeNR4G2fVogAlAgAKIIgCYCYMICMC5OBPHi/e+//VTcEvSg5avQ7sWJIgCiCIAmAmCCnwj2oLr7d8wTwTpBAEQRAE0EwIQGoL7d53pIALpAAEQRAE0EwIS+HkC99PMYtPzqYASgQABEEQBNzM0E3wcwf+zPJMvOW74K7V6cKAIgikWiibmZ4DuXm10AAB5cSURBVEcBnXxvb10O274PmAAYAiCKRaKJuZnAF4T5cZhlJ2ffni1eF6a9V4UhAAUCIIpFoom5mUNOB93yOaEJQIEAiGKRaGJuhgA4QQBEsUg0MTdDAJwgAKJYJJqYm+F00E4QAFEsEk3MzRAAJwiAKBaJJuZmCIATBEAUi0QTczMEwAkCIIpFoom5GQLgBAEQxSLRxNwMAXCCAIhikWhiboYAOEEARLFINDE3QwCcIACiWCSamJshAE4QAFEsEk3MzRAAJwiAKBaJJuZmCIATBEAUi0QTczMEwAkCIIpFoom5mV0BuHl+tqa9lwIor0Krl6aKAIhikWhibmZnADo9EWh5FVq9NFUEQBSLRBNzMwTACQIgikWiibmZ/QNw8vV3n9q9Cq1emioCIIpFoom5mcA7gS+y7Js/8v+dvsuy03b3PwEwBEAUi0QTczNhAZhk2Xn15sXizbauQrsXJ4oAiGKRaGJuJigA09eL2/1vnrb9KwABKBAAUSwSTczNhL4mcDMA3AncAQIgikWiibmZ4BeFf1W9PeFRQJ0gAKJYJJqYmwm9CSg7eWlvXg5bvxeYABQIgCgWiSbmZsLuBL6wB4CenZ3Z/3IncAcIgCgWiSbmZsICUPwKUONhoF0gAKJYJJqYmwl8HkCjAE9a3v8EwBAAUSwSTczNBJ8N9Lc3D/Ptf+/xh/avQuuX6NRgcMsHCYAoFokm5mY4HXQ8BCBFLBJNzM0QgHgIQIpYJJqYmwkOwPSn58PiGQA3/9r6VWj7Ar0iAClikWhibiY0AL8OqxNBT9p+FCgBMARAFItEE3Mz4WcDrQIwzk7etnwV2r04vwhAilgkmpibCT4baJY9LAJQPB70QctXod2L84sApIhFoom5mbAAjLPs9EN5GrgLTgVxKAKQIhaJJuZmQs8FdPK2Og8oZwM9GAFIEYtEE3Mze5wOmgDcEQFIEYtEE3MzoQGofwO4HhKAAxGAFLFINDE3E3w66NNPFgDuBD4cAUgRi0QTczN7PArobJid/FPxxquNn3P4VWj34vwiAClikWhibibweQBjTgd9dwQgRSwSTczNhJ4O+l1n+58AFEYjCqCJRaKJuZnw00E/t/X/6Pv2r0Lrl+jU9gCM5mJeHbSCRaKJuZm9zgb6ezdXoZNLdWhrAEYjCiCLRaKJuZmwRwH9y5++m9/wc/384ZOWr0K7F+fXtgCMRhRAF4tEE3MzezwRrH6bU0EcZksARiMKIIxFoom5mb0DwBPBDkYAUsQi0cTczM4ANB8AmtUnhW71KrR6aY5tDsBoVeyrhTthkWhibmZnAK6HawHgJqDDEIAUsUg0MTez+yag9V8BeCbwYQhAilgkmpib2R2A6+dnD/Otf++s9Oi7D21fhZYvzy0CkCIWiSbmZva+E7iDq9DZJTvDncApYpFoYm5m7+cBdHAVOrtkZwhAilgkmpib2euZwB1dhfhf8jgIQIpYJJqYmwkNwOXZW/vf6+ETTgZ3IJ4JnCIWiSbmZgIDcJFl5/M3Wn49GALAuYCUsUg0MTcTFoDiyQC294sXBONhoAfibKApYpFoYm4mLADj+dO/7HUBeEnIw/B6AClikWhibib4NYHPq7cnPBP4ULwiWIpYJJqYmznkbKCcC+gwBCBFLBJNzM0QgHgIQIpYJJqYmwm+CWh+zy83AR2MAKSIRaKJuZngO4FPyucBfBxyJ/ChCECKWCSamJsJC8CkOAnoydnZGWcDvQMCkCIWiSbmZgKfCNY8JzRPBDsQAUgRi0QTczOBAbDH/5dafkl4AlAiAKJYJJqYmwk+GdznN/aqAI/bfjUAAlAiAKJYJJqYm+FsoPEQgBSxSDQxN0MA4iEAKWKRaGJuhgDEQwBSxCLRxNzMrgDcPD979Ev+f5oe8UzggxCAFLFINDE3szMAxZkf8v/TxKkgDkMAUsQi0cTcDAGIhwCkiEWiibkZAhAPAUgRi0QTczPcCRwPAUgRi0QTczMEIB4CkCDmJooAGAIQDwFIEHMTRQAMAYiHACSIuYkiACbgeQBreB7AYQhAgpibKAJgAh4FtIZHAR2GACSIuYkiAIYAxEMAEsTcRBEAs38ATr7+jtcEPggBSBBzE0UATOCdwBdZ9s0fs/KFYVp+TXgCYFgkmpibKAJggl8T+Lx682LxZltXod2L84sAJIi5iSIAJigA09eL2/1vnrb9KwABKLBINDE3UQTABAXATgi04e2WrkKrl+YYAUgQcxNFAExoALJX1dsTHgV0KAKQIOYmigCY0JuAspOX9ublsPV7gQlAgUWiibmJIgAm7E7gC3sA6NnZmf0vdwIfhgAkiLmJIgAmLADFrwA1HgZ6IAKQIOYmigCYwOcBNArwpOX9TwAMi0QTcxNFAEzw2UB/e/Mw3/73Hn9o/yq0folOEYAEMTdRBMBwOuh4CECCmJsoAmAIQDwEIEHMTRQBMMEBmP70fFg8A+DmX1u/Cm1foFcEIEHMTRQBMKEB+HVYnQh60vajQAmAYZFoYm6iCIAJPxtoFYBxdvK25avQ7sX5RQASxNxEEQATfDbQLHtYBKB4POiDlq9CuxfnFwFIEHMTRQBMWADGWXb6oTwN3AWngjgUAUgQcxNFAEzouYBO3lbnAeVsoAcjAAlibqIIgNnjdNAE4I4IQIKYmygCYEIDUP8GcD0kAAciAAlibqIIgAk+HfTpJwsAdwIfjgAkiLmJIgBmj0cBnQ2zk38q3ni18XMOvwrtXpxfBCBBzE0UATCBzwMYczrouyMACWJuogiACT0d9LvO9j8BMCwSTcxNFAEw4aeDfm7r/9H37V+F1i/RKQKQIOYmigCYvc4G+ns3V6GTS3WIACSIuYkiACYsAL++aPt2n+ZV6O6ifSEACWJuogiACX0YaOu3/DeuQmeX7AwBSBBzE0UAzB7PBO7sKnR2yc4QgAQxN1EEwOxxLqDOrkJnl+wMAUjPaMTgNBEAE/xEsPvtvxr8/Cp0dcHeEIDUjOaOfUWwNwJgwn4D+Pub4iGg985KjzgX0EEIQGJGIwogiwCY0PsAmjgZ3GEIQFpGIwqgiwAYAhAPAUjKaEQBhBEAQwDiIQBJIQDSCIDZ65nAHV2F+F/yOAhASkarjn2FsBcCYAhAPAQgJQRAGwEwBCAeApASAqCNAJiAAEw/Pj87+7r9s4DWV6GzS3aGAKSEAGgjAGZ3AD4/6+h1AOqr0NHlukMAksL+l0YAzM4ALB4B1FUBCECBDaKGAEgjAGZnAC4WD/9s+bWA66vQzcX6QwCSQgCkEQCzKwDT18UD/9//3+L3gAcdXYVuLtYfApAW9r8yAmCCAvDKTgfX1W1ABKDACtHD/hdGAMyuABR3ARSngr4etv4M4PoqdHKpDhGA1LD/dREAExKAYvHP/7eLq9DJpTpEANLD9ldFAAwBiOfWAHBAamL/i+L7zRCAeAhAggiAKL7fDAGIhwAkiACI4vvNhASgeCmwh42XBOMVwQ5DABJEAETx/WaCArCC1wM4DAFIEAEQxfebIQDxEIAEEQBRfL8ZAhAPAUgQARDF95vh9QDiIQAJIgCi+H4zBCAeApAgAiCK7zdDAOIhAAkiAKL4fjMEIB4CkCACIIrvN0MA4iEACSIAovh+MwQgHgKQIAIgiu83QwDiIQAJIgCi+H4zBCAeApAgAiCK7zdDAOIhAAkiAKL4fjMEIB4CkCACIIrvN0MA4iEACSIAovh+MwQgHgKQIAIgiu83QwDiIQAJIgCi+H4zBCAeApAgAiCK7zdDAOIhAAkiAKL4fjMEIB4CkCACIIrvN0MA4iEACSIAovh+MwQgHgKQIAIgiu83QwDiIQAJIgCi+H4zBCAeApAgAiCK7zdDAOIhAAkiAJqYW4kAxEMAEsQi0cTcSgQgHgKQIBaJJuZWIgDxEIAEsUg0MbcSAYiHACSIRaKJuZUIQDwEIEEsEk3MrUQA4iEACWKRaGJuJQIQDwFIEItEE3MrEYB4CECKmJskAlAiAPEQgBQxN0kEoEQA4iEAKWJukghAiQDEQwBSxNwkEYASAYiHAKSIuUkiACUCEA8BSBFzk0QASgQgHgKQIuYmiQCUCEA8BCBFzE0SASgRgGgGg1sLwCLRxNwkEYASAYhkMLftE1gkmpibJAJQIgBxDAa7CsAi0cTcJBGAEgGIYjDYWQAWiSbmJokAlAhADIPB7gKwSDQxN0kEoEQAYiAA6WJukghAiQBEMFi16ZNYJJqYmyQCUCIAERCAhDE3SQSgRAAiIAAJY26SCECJAERAABLG3CQRgBIBiIE7gdPF3CQRgFIbAfj8PMuyRx9u/+OLrPJg7Soc8CXFEIB0MTdJBKDUQgAm5WI/eXXrH48JAAFIEXOTRABKdw/A9TA7/TD7/Do7eXvLH988Xf5w8yrs/SX17N7/LBJRzE0SASjdPQDj7PRT/j/T18s/26/88fXQ3t14Ffb+koJ27n8WiSjmJokAlO4cgJunWXkjzyT76pftfzxZv+mnvgr7fklJu/Y/i0QUc5NEAEp3DkC99+uVv/GPL7JXG/5yeRX2/ZKibtv+MxaJKuYmiQCU7hyAi6y6bWf6Ojvf/sfjr/79zTDLHr3fcBX2/ZKqeEWwFDE3SQSgdOcAjBub/sHWP85/D6icr10CATAsEk3MTRIBKLUQgAdrb63/8fUwO3nxafbl3YYCEADDItHE3CQRgFKkAEyy6j6A8eKu4r/O/aMnBoNjXwMAZjQ69jXwIVIAajdP134F4DcAw0+SmpibJH4DKMUOwOrTBWYEoMIi0cTcJBGAUqQ7gZsfIAAbsUg0MTdJBKAU62GgNQKwBYtEE3OTRABKsZ4INrfagxkBqLBINDE3SQSgFOlUEPUtQtfDtacEEwDDItHE3CQRgFKkk8FNqpOC5u+unRSOABgWiSbmJokAlCKdDjpf/CcvZ7PPz7L1cwIRAMMi0cTcJBGAUgsvCHO59Mov9VO+lv+4WP327sv1q7D/l9REAFLE3BSNRhTAtP6SkHUAVl4Scvrjw3z9P1574UgCUGGRaGJuekZzx74ix8eLwsdDAFLE3OSMRhRgjgDEQwBSxNzUjEYUoEYA4iEAKWJuYkYjCrBAAOIhAClibmIIQBMBiIcApIi5aRmtOvYVOi4CEA8BSBFz00IAlhCAeAhAipibFgKwhADEQwBSxNy0EIAlBCAeApAi5iaG/d9EAOIhAClibmIIQBMBiIcAaGJuSSEATQQgHhaJJuaWFvZ/AwGIh0Wiibklhv2/QADiYZFoYm6pYf/XCEA8LBJNzC09bP8KAYiHRaKJuSWI/V8iAPGwSDQxtwQRgBIBiIdFoom5JYgAlAhAPCwSTcwtQQSgRADiYZFoYm4JIgAlAhAPi0QTc0sQASgRgHhYJJqYW4IIQIkAxMMi0cTcEkQASgQgHhaJJuaWIAJQIgDxsEg0MbcEEYASAYiHRaKJuSWIAJQIQDwsEk3MLUEEoEQA4mGRaGJuCSIAJQIQD4tEE3NLEAEoEYB4WCSamFuCCECJAMTDItHE3BJEAEoEIB4WiSbmliACUCIA8bBINDG3BBGAEgGIh0WiibkliACUCEA8LBJNzC1BBKBEAOJhkWhibgkiACUCEA+LRBNzSxABKBGAeFgkmphbgghAiQDEwyLRxNwSRABKBCAeFokm5pYgAlAiAPGwSDQxtwQRgBIBiIdFoom5JYgAlAhAPCwSTcwtQQSgRADiYZFIGgxuHRxzk0QASgQgHgIgaDC37ROYmyQCUCIA8RAAPYPBrgIwN0kEoEQA4iEAcgaDnQVgbpIIQIkAxEMA1AwGuwvA3CQRgBIBiIcAqCEAySIAJQIQDwEQM1i16ZOYmyQCUCIA8RAAMQRA2q3fbwSgRADiIQBiCIA0AhCAAMRDAMQQAGkEIAABiIcAqOFOYGUEIAABiIcAqCEAyghAAAIQDwFQQwCUEYAABCAeAiBn9/5nbm4RgAAEIB4CoGfn/mdubhGAAAQgHgIgaNf+Z25uEYAABCAeAiDptu0/Y25+EYAABCAeAqCJuWlibgEIQDwckJqYmybmFoAAxMMBqYm5aWJuAQhAPByQmpibJuYWgADEwwGpiblpYm4BCEA8HJCamJsm5haAAMTDAamJuWlibgEIQDwckJqYmybmFoAAxMMBqYm5aWJuAQhAPByQmpibJuYWgADEwwGpiblpYm4BCEA8HJCamJsm5haAAMTDAamJuWlibgEIQDwckJqYmybmFoAAxMMBqYm5aWJuAQhAPByQmpibJuYWgADEwwGpiblpYm4BCEA8HJCamJsm5haAAMTDAamJuWlibgEIQDwckJqYmybmFoAAxMMBqYm5aWJuAQhAPByQmpibJuYWgADEwwGpiblpYm4BCEA8HJCamJsm5haAAMTDAamJuWlibgEIQDwckJqYmybmFoAAxMMBqYm5aWJuAQhAPByQmpibJuYWgADEwwGpiblpYm4BCEA8HJCamJsm5haAAMTDAamJuWlibgEIQDwckJqYmybmFoAAxMMBmSLm5hXfbwEIQDwckClibl7x/RaAAMTDAZki5uYV328BCEA8HJApYm5e8f0WgADEwwGZIubmFd9vAQhAPByQKWJuXvH9FoAAxMMBmSLm5hXfbwEIQDwckClibl7x/RaAAMTDAZki5ubUYHDrNxxzMwQgHgKQIubm0mBu2ycwN0MA4iEAKWJuHg0GuwrA3AwBiIcApIi5OTQY7CwAczMEIB4CkCLm5s9gsLsAzM0QgHgIQIqYmz8EIBgBiIcApIi5uTNYtemTmJshAPEQgBQxN3cIQDgCEA8BSBFzc4cAhCMA8RCAFDE3dwhAOAIQDwFIEXPzhzuBgxGAeAhAipibPwQgGAGIhwCkiLn5QwCCEYB4CECKmJtDu/c/cysRgHgIQIqYm0c79z9zKxGAeAhAipibS7v2P3MrEYB4CECKmJtTt23/GXOrEIB4CECKmJtXfL8FIADxcECmiLl5xfdbAAIQDwdkipibV3y/BSAA8XBApoi5ecX3WwACEA8HZIqYm1d8vwUgAPFwQKaIuXnF91sAAhAPB2SKmJtXfL8FIADxcECmiLl5xfdbAAIQDwdkipibV3y/BSAA8XBApoi5ecX3WwACEA8HZIqYm1d8vwUgAF5wQGpibl4RgAAEwAsOSE3MzSsCEIAAeMEBqYm5eUUAAhAALzggNTE3rwhAAALgBQekJubmFQEIQAC84IDUxNy8IgABCIAXHJCamJtXBCAAAfCCA1ITc/OKAAQgAF5wQGpibl4RgAAEwAsOSE3MzSsCEIAAeMEBqYm5eUUAAhAALzggNTE3rwhAAALgBQekJubmFQEIQAC84IDUxNy8IgABCIAXHJCamJtXBCAAAfCCA1ITc/OKAAQgAF5wQGpibl4RgAAEwAsOSE3MzSsCEIAAeMEBqYm5eUUAAhAALzggNTE3rwhAAALgBQekJubmFQEIQAC84IDUxNy8IgABCIAXHJCamJtXBCAAAfCCA1ITc/OKAAQgAF5wQGpibl4RgAAEwAsOSE3MzSsCEIAAeMEBqYm5eUUAAhAALzggNTE3rwhAAALgBQekJubmFQEIQAC84IDUxNy8IgABCIAXHJCamJtXBCAAAfCCA1ITc/OKAAQgAF5wQGpibl4RgAAEwAsOSE3MzSsCEIAAeMEBqYm5eUUAAhAALzggNTE3rwhAAALgBQekJubmFQEIQAC84IDUxNw0MTdDALzggNTE3DQxN0MAvOCA1MTcNDE3QwC84IDUxNw0MTdDALzggNTE3DQxN0MAvOCA1MTcNDE3QwC84IDUxNw0MTdDALzggNTE3DQxN0MAvOCA1MTcNDE3QwC84IDUxNw0MTdDALzggNTE3DQxN0MAvOCA1MTcNDE3QwC84IDUxNw0MTdDALzggNTE3DQxN0MAvOCA1MTcNDE3QwC84IDUxNw0MTdDALzggNTE3DQxN0MAvOCA1MTcNDE3QwC84IDUxNw0MTdDALzggNTE3DQxN0MAvOCA1MTcNDE3QwC84IDUxNw0MTdDALzggNTE3DQxN0MAvOCA1MTcNDE3QwC84IDUxNw0MTdDALzggNTE3DQxN0MAvOCA1MTcNDE3QwC84IDUxNw0MTdDALzggNTE3DQxN0MAvOCA1MTcNDE3QwC84IDUxNw0MTdDALzggNTE3DQxN0MAvOCA1MTcNDE3QwC84IDUxNw0MTdDALzggNTE3DQxN0MAvOCA1MTcNDE34yEAAICjIAAA0FNHDwAMt4RpYm6amNtGBOBYOCA1MTdNzG0jAnAsHJCamJsm5rYRATgWDkhNzE0Tc9uIABwLB6Qm5qaJuW1EAI6FA1ITc9PE3DYiAADQUwQAAHqKAABATxEAAOgpAgAAPUUAAKCnCAAA9BQBAICeIgBd+fLjwyzLHn3Y/O62T5t9fJZlJ08+RbuaWHX5sJjAH/N3Pz/fOLfZ5zfDLLv3sprURVZ5EO16wtw8zc7vfCH9HSYB6MhkWB1E55ve3fZp03H53snb2NcXpenr5QlMqndfrXzeZTW403JpjHu0M3xpIwA9HiYB6Mb1MLv/Pv/JIl8nr9bf3fZpxaF3+mE2fUcBjmWcnbzMB/Is++qX4t18QPlA8vmsDCTvwv18UD9US+LmKQM7khYC0OdhEoBuXFQLJP+BsvixYuXdbZ+Wr5sH1Z/34IcPj/IJWInna2Vcziufz9JA6jlWA7wennKr3XHcPQC9HiYB6MRiYUyKnx1X3t32afkRWH20Vz+EeDIvcr75i8nk26X8jW0y//NZ9W41nyoYE4J9LHcPQK+HSQA6dj1cWuUr7678+Xj++0FehtUbnRFVGYB679clmH90+YfEC8YV0Zcfz4q7ZR7bPfNFAD6/aT5y4qPdbf998WbzG8q2+m+Lj9V6PUwC0LHlH/lX313583H9s8e4hYc24HDVD4MXjQXSGMjye7nxV/9ePI7k0fuIV7G36gdO2IjyAHw9bNxvP31XfbAIwuoP9/OPNTd+v4dJADo2Xr7tYOXdlT+vA7B6mzPi+jgsd8TaT5Cl4veBafFw0Xsv5+9vfpAX2pf/xz7Nf/afXg7te6b4b1/cb39Zj6xY/UUGHjRuHypv2rvIsm/yj/06ZJhzBKBb10vH2uq7q39+0bjFgQAciz0S9Btb/M1fyRoDycf18nXjZ8n8/ZMXn2Zf3vVjaRxX/U1S3mNW7Gu7ySYfwvn8/9YfXboTvxibfWzp9/B+D5MAdCo/8Jo/8a+8u/bnjaOXABzNzT+fDbPscfnj5LYANH/OLB5H+Kr6rM2/4KEDk3kA6ofv5280f4Y6r1d9eQtQfYPe0jT7PUwC0KXp8uP+p6tPA1j788XvrwTgqKrbgG4JQPPnzForT0vFbtPf/te32TwA1X/yYvc3bqqzHlQftSw0buy/aNwL0O9hEoAOFYv9fOu7m/58/jzU8zEBOK7y58DtAagWyMqS4L6bGOxhPvO7fRePzyp+2l9b8paBciyLG/dzix/u+z1MAtCdrfv/uvmoheVPm/5YPP7gw9ojExBZ+d2/fCdwPbfFXTmrS4Jwd8/O1HDy9feXGwLQ2OHljUH2QN7yFqDtAejxMAlAZ26eZdV9TqvvLgVg5dOqT+7Db5++2Xf/8sNA67k1xjPu3844sosse2yP0JyE/AZgH76YP15o0zdVv4dJALry+enSGcRW3t3x59ueL4ZYyp8GtzwRbPGj4soPjfzm1rnFf/GLgPsAbItXf7jlJp1+D5MAdCQ/Lps7fOXdLX9ePzztog8PQPBovHx78LZTQaw81qT+W/NTCaEz9cKfvp4HoNra1e9sS5Oxyf3v4fxUixu/q3o9TALQjeny+SOna6eT3Pjn84eB1g8HRWSNxwDaYLacDK6+3Xhc3dBcPQt15Vx/aF89ikl9J3D5H78c3crzAOzj3y5O7fpg+TJmS3/cx2ESgG5cLD/g82Lz4z/X/rw8GfHHYR8OPZeKIr/8NPvyJqtTvPF00MUN0X/Y43XP53/LTiLdg58Zj21cPp33XXUuiFufCVx9fuPN4gRCvz1bHmefh0kAOrH0gIPz1Xe3fVp5j3DhPjcAHcl8AvMxXVYPOFzdBfUZZ+y9z9XfOlm7Ox9tm3/T3P+3YXUr3Z+HjW+apXMBFSZZvcnrj62Ms8fDJACduK7PV2WrZOXdbZ82Kx4GWpyU5AU//x+NnRamOtNkYdtLQi6fV9Lm1vhb6M703dC+R+pH97+ys4HW3zTl2UDrU7nln7C46d8+dm/xgp+V/g6TAABATxEAAOgpAgAAPUUAAKCnCAAA9BQBAICeIgAA0FMEAAB6igAAQE8RAADoKQIAAD1FAACgpwgAAPQUAQCAniIAANBTBAAAeooAAEBPEQAA6CkCAAA99R/b8+0Ji9NB3AAAAABJRU5ErkJggg==" class="img-fluid figure-img" width="768">
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-fig_d2-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure 7: Predicted probabilities of voting for a green party by education and age group (Appendix)
</figcaption>
</figure>
</div>
</div>
</div>
</section>
<section id="multilevel-model-voting-for-the-greens-three-way-interaction" class="level2" data-number="3.4">
<h2 data-number="3.4" class="anchored" data-anchor-id="multilevel-model-voting-for-the-greens-three-way-interaction"><span class="header-section-number">3.4</span> Multilevel model: Voting for the greens (three-way interaction)</h2>
<div class="cell">
<details class="code-fold">
<summary>Show the code</summary>
<div class="sourceCode cell-code" id="cb10"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a><span class="co"># model</span></span>
<span id="cb10-2"><a href="#cb10-2" aria-hidden="true" tabindex="-1"></a>three_green <span class="ot">&lt;-</span> <span class="fu">glmer</span>(</span>
<span id="cb10-3"><a href="#cb10-3" aria-hidden="true" tabindex="-1"></a>  vote_green <span class="sc">~</span></span>
<span id="cb10-4"><a href="#cb10-4" aria-hidden="true" tabindex="-1"></a>    cohort_fct <span class="sc">*</span> edu_fct <span class="sc">*</span> gndr_fct <span class="sc">+</span></span>
<span id="cb10-5"><a href="#cb10-5" aria-hidden="true" tabindex="-1"></a>    age_grp_fct <span class="sc">*</span> edu_fct <span class="sc">*</span> gndr_fct <span class="sc">+</span></span>
<span id="cb10-6"><a href="#cb10-6" aria-hidden="true" tabindex="-1"></a>    <span class="fu">factor</span>(essround) <span class="sc">*</span> edu_fct <span class="sc">*</span> gndr_fct <span class="sc">+</span></span>
<span id="cb10-7"><a href="#cb10-7" aria-hidden="true" tabindex="-1"></a>    immigrant <span class="sc">+</span> <span class="fu">factor</span>(cntry_id) <span class="sc">+</span></span>
<span id="cb10-8"><a href="#cb10-8" aria-hidden="true" tabindex="-1"></a>    (<span class="dv">1</span> <span class="sc">|</span> cntry_wave_id),</span>
<span id="cb10-9"><a href="#cb10-9" aria-hidden="true" tabindex="-1"></a>  <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">&quot;logit&quot;</span>),</span>
<span id="cb10-10"><a href="#cb10-10" aria-hidden="true" tabindex="-1"></a>  <span class="at">nAGQ =</span> <span class="dv">0</span>,</span>
<span id="cb10-11"><a href="#cb10-11" aria-hidden="true" tabindex="-1"></a>  <span class="at">data =</span> dfgreen</span>
<span id="cb10-12"><a href="#cb10-12" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb10-13"><a href="#cb10-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb10-14"><a href="#cb10-14" aria-hidden="true" tabindex="-1"></a><span class="co"># predicted probabilities</span></span>
<span id="cb10-15"><a href="#cb10-15" aria-hidden="true" tabindex="-1"></a>pp_three_green <span class="ot">&lt;-</span> <span class="fu">predictions</span>(three_green,</span>
<span id="cb10-16"><a href="#cb10-16" aria-hidden="true" tabindex="-1"></a>  <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&quot;gndr_fct&quot;</span>, <span class="st">&quot;cohort_fct&quot;</span>, <span class="st">&quot;edu_fct&quot;</span>),</span>
<span id="cb10-17"><a href="#cb10-17" aria-hidden="true" tabindex="-1"></a>  <span class="at">newdata =</span> <span class="fu">datagrid</span>(</span>
<span id="cb10-18"><a href="#cb10-18" aria-hidden="true" tabindex="-1"></a>    <span class="at">gndr_fct =</span> <span class="fu">c</span>(<span class="st">&quot;men&quot;</span>, <span class="st">&quot;women&quot;</span>),</span>
<span id="cb10-19"><a href="#cb10-19" aria-hidden="true" tabindex="-1"></a>    <span class="at">cohort_fct =</span> <span class="fu">c</span>(</span>
<span id="cb10-20"><a href="#cb10-20" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;interwar&quot;</span>,</span>
<span id="cb10-21"><a href="#cb10-21" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;post-war&quot;</span>,</span>
<span id="cb10-22"><a href="#cb10-22" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;60-70s&quot;</span>,</span>
<span id="cb10-23"><a href="#cb10-23" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;80s&quot;</span>,</span>
<span id="cb10-24"><a href="#cb10-24" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;90s&quot;</span>,</span>
<span id="cb10-25"><a href="#cb10-25" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;millennial&quot;</span></span>
<span id="cb10-26"><a href="#cb10-26" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb10-27"><a href="#cb10-27" aria-hidden="true" tabindex="-1"></a>    <span class="at">edu_fct =</span> <span class="fu">c</span>(</span>
<span id="cb10-28"><a href="#cb10-28" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;(below) lower secondary&quot;</span>,</span>
<span id="cb10-29"><a href="#cb10-29" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;upper secondary&quot;</span>,</span>
<span id="cb10-30"><a href="#cb10-30" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;tertiary&quot;</span></span>
<span id="cb10-31"><a href="#cb10-31" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb10-32"><a href="#cb10-32" aria-hidden="true" tabindex="-1"></a>    <span class="at">grid_type =</span> <span class="st">&quot;counterfactual&quot;</span></span>
<span id="cb10-33"><a href="#cb10-33" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb10-34"><a href="#cb10-34" aria-hidden="true" tabindex="-1"></a>    <span class="at">re.form =</span> <span class="cn">NA</span></span>
<span id="cb10-35"><a href="#cb10-35" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb10-36"><a href="#cb10-36" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb10-37"><a href="#cb10-37" aria-hidden="true" tabindex="-1"></a>plot_pp_three_green <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(</span>
<span id="cb10-38"><a href="#cb10-38" aria-hidden="true" tabindex="-1"></a>  <span class="at">data =</span> pp_three_green,</span>
<span id="cb10-39"><a href="#cb10-39" aria-hidden="true" tabindex="-1"></a>  <span class="fu">aes</span>(</span>
<span id="cb10-40"><a href="#cb10-40" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> cohort_fct, <span class="at">y =</span> estimate, <span class="at">ymin =</span> conf.low,</span>
<span id="cb10-41"><a href="#cb10-41" aria-hidden="true" tabindex="-1"></a>    <span class="at">ymax =</span> conf.high, <span class="at">color =</span> edu_fct</span>
<span id="cb10-42"><a href="#cb10-42" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb10-43"><a href="#cb10-43" aria-hidden="true" tabindex="-1"></a>) <span class="sc">+</span></span>
<span id="cb10-44"><a href="#cb10-44" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept =</span> <span class="dv">0</span>, <span class="at">color =</span> <span class="st">&quot;grey70&quot;</span>) <span class="sc">+</span></span>
<span id="cb10-45"><a href="#cb10-45" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_pointrange</span>(<span class="at">position =</span> <span class="fu">position_dodge</span>(<span class="at">width =</span> <span class="fu">c</span>(<span class="fl">0.2</span>))) <span class="sc">+</span></span>
<span id="cb10-46"><a href="#cb10-46" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb10-47"><a href="#cb10-47" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> <span class="cn">NULL</span>, <span class="at">y =</span> <span class="st">&quot;Predicted probability&quot;</span>,</span>
<span id="cb10-48"><a href="#cb10-48" aria-hidden="true" tabindex="-1"></a>    <span class="at">color =</span> <span class="cn">NULL</span></span>
<span id="cb10-49"><a href="#cb10-49" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb10-50"><a href="#cb10-50" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_viridis_d</span>(<span class="at">option =</span> <span class="st">&quot;plasma&quot;</span>, <span class="at">end =</span> .<span class="dv">8</span>) <span class="sc">+</span></span>
<span id="cb10-51"><a href="#cb10-51" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_light</span>() <span class="sc">+</span></span>
<span id="cb10-52"><a href="#cb10-52" aria-hidden="true" tabindex="-1"></a>  <span class="fu">my_theme</span>() <span class="sc">+</span></span>
<span id="cb10-53"><a href="#cb10-53" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;top&quot;</span>) <span class="sc">+</span></span>
<span id="cb10-54"><a href="#cb10-54" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>gndr_fct)</span>
<span id="cb10-55"><a href="#cb10-55" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb10-56"><a href="#cb10-56" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(</span>
<span id="cb10-57"><a href="#cb10-57" aria-hidden="true" tabindex="-1"></a>  <span class="at">filename =</span> <span class="fu">here</span>(<span class="st">&quot;figs&quot;</span>, <span class="st">&quot;final&quot;</span>, <span class="st">&quot;fig_2_upper.png&quot;</span>),</span>
<span id="cb10-58"><a href="#cb10-58" aria-hidden="true" tabindex="-1"></a>  <span class="at">units =</span> <span class="fu">c</span>(<span class="st">&quot;in&quot;</span>),</span>
<span id="cb10-59"><a href="#cb10-59" aria-hidden="true" tabindex="-1"></a>  <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">5</span>,</span>
<span id="cb10-60"><a href="#cb10-60" aria-hidden="true" tabindex="-1"></a>  <span class="at">dpi =</span> <span class="dv">300</span></span>
<span id="cb10-61"><a href="#cb10-61" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb10-62"><a href="#cb10-62" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb10-63"><a href="#cb10-63" aria-hidden="true" tabindex="-1"></a><span class="co"># marginal effects</span></span>
<span id="cb10-64"><a href="#cb10-64" aria-hidden="true" tabindex="-1"></a>me_three_green_edu <span class="ot">&lt;-</span> <span class="fu">avg_slopes</span>(</span>
<span id="cb10-65"><a href="#cb10-65" aria-hidden="true" tabindex="-1"></a>  three_green,</span>
<span id="cb10-66"><a href="#cb10-66" aria-hidden="true" tabindex="-1"></a>  <span class="at">variables =</span> <span class="st">&quot;edu_fct&quot;</span>,</span>
<span id="cb10-67"><a href="#cb10-67" aria-hidden="true" tabindex="-1"></a>  <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&quot;cohort_fct&quot;</span>, <span class="st">&quot;gndr_fct&quot;</span>),</span>
<span id="cb10-68"><a href="#cb10-68" aria-hidden="true" tabindex="-1"></a>  <span class="at">newdata =</span> <span class="fu">datagrid</span>(</span>
<span id="cb10-69"><a href="#cb10-69" aria-hidden="true" tabindex="-1"></a>    <span class="at">gndr_fct =</span> <span class="fu">c</span>(<span class="st">&quot;men&quot;</span>, <span class="st">&quot;women&quot;</span>),</span>
<span id="cb10-70"><a href="#cb10-70" aria-hidden="true" tabindex="-1"></a>    <span class="at">cohort_fct =</span> <span class="fu">c</span>(</span>
<span id="cb10-71"><a href="#cb10-71" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;interwar&quot;</span>,</span>
<span id="cb10-72"><a href="#cb10-72" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;post-war&quot;</span>,</span>
<span id="cb10-73"><a href="#cb10-73" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;60-70s&quot;</span>,</span>
<span id="cb10-74"><a href="#cb10-74" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;80s&quot;</span>,</span>
<span id="cb10-75"><a href="#cb10-75" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;90s&quot;</span>,</span>
<span id="cb10-76"><a href="#cb10-76" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;millennial&quot;</span></span>
<span id="cb10-77"><a href="#cb10-77" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb10-78"><a href="#cb10-78" aria-hidden="true" tabindex="-1"></a>    <span class="at">edu_fct =</span> <span class="fu">c</span>(</span>
<span id="cb10-79"><a href="#cb10-79" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;(below) lower secondary&quot;</span>,</span>
<span id="cb10-80"><a href="#cb10-80" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;upper secondary&quot;</span>,</span>
<span id="cb10-81"><a href="#cb10-81" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;tertiary&quot;</span></span>
<span id="cb10-82"><a href="#cb10-82" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb10-83"><a href="#cb10-83" aria-hidden="true" tabindex="-1"></a>    <span class="at">grid_type =</span> <span class="st">&quot;counterfactual&quot;</span></span>
<span id="cb10-84"><a href="#cb10-84" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb10-85"><a href="#cb10-85" aria-hidden="true" tabindex="-1"></a>    <span class="at">re.form =</span> <span class="cn">NA</span></span>
<span id="cb10-86"><a href="#cb10-86" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb10-87"><a href="#cb10-87" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb10-88"><a href="#cb10-88" aria-hidden="true" tabindex="-1"></a>me_three_green_edu <span class="ot">&lt;-</span> me_three_green_edu <span class="sc">%&gt;%</span></span>
<span id="cb10-89"><a href="#cb10-89" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">contrast =</span> <span class="fu">case_when</span>(</span>
<span id="cb10-90"><a href="#cb10-90" aria-hidden="true" tabindex="-1"></a>    contrast <span class="sc">==</span> <span class="st">&quot;tertiary - (below) lower secondary&quot;</span> <span class="sc">~</span></span>
<span id="cb10-91"><a href="#cb10-91" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;high vs low edu&quot;</span>,</span>
<span id="cb10-92"><a href="#cb10-92" aria-hidden="true" tabindex="-1"></a>    contrast <span class="sc">==</span> <span class="st">&quot;upper secondary - (below) lower secondary&quot;</span> <span class="sc">~</span></span>
<span id="cb10-93"><a href="#cb10-93" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;medium vs low edu&quot;</span></span>
<span id="cb10-94"><a href="#cb10-94" aria-hidden="true" tabindex="-1"></a>  ))</span>
<span id="cb10-95"><a href="#cb10-95" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb10-96"><a href="#cb10-96" aria-hidden="true" tabindex="-1"></a>plot_me_three_green <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(<span class="at">data =</span> me_three_green_edu, <span class="fu">aes</span>(</span>
<span id="cb10-97"><a href="#cb10-97" aria-hidden="true" tabindex="-1"></a>  <span class="at">x =</span> cohort_fct, <span class="at">y =</span> estimate,</span>
<span id="cb10-98"><a href="#cb10-98" aria-hidden="true" tabindex="-1"></a>  <span class="at">ymin =</span> conf.low, <span class="at">ymax =</span> conf.high,</span>
<span id="cb10-99"><a href="#cb10-99" aria-hidden="true" tabindex="-1"></a>  <span class="at">color =</span> contrast</span>
<span id="cb10-100"><a href="#cb10-100" aria-hidden="true" tabindex="-1"></a>)) <span class="sc">+</span></span>
<span id="cb10-101"><a href="#cb10-101" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept =</span> <span class="dv">0</span>, <span class="at">color =</span> <span class="st">&quot;grey70&quot;</span>) <span class="sc">+</span></span>
<span id="cb10-102"><a href="#cb10-102" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_pointrange</span>(<span class="at">position =</span> <span class="fu">position_dodge</span>(<span class="at">width =</span> <span class="fu">c</span>(<span class="fl">0.2</span>))) <span class="sc">+</span></span>
<span id="cb10-103"><a href="#cb10-103" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb10-104"><a href="#cb10-104" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> <span class="cn">NULL</span>, <span class="at">color =</span> <span class="st">&quot;Education&quot;</span>,</span>
<span id="cb10-105"><a href="#cb10-105" aria-hidden="true" tabindex="-1"></a>    <span class="at">y =</span> <span class="st">&quot;Marginal effect of education&quot;</span></span>
<span id="cb10-106"><a href="#cb10-106" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb10-107"><a href="#cb10-107" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;#fe9f6d&quot;</span>, <span class="st">&quot;#8c2981&quot;</span>)) <span class="sc">+</span></span>
<span id="cb10-108"><a href="#cb10-108" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>gndr_fct) <span class="sc">+</span></span>
<span id="cb10-109"><a href="#cb10-109" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_light</span>() <span class="sc">+</span></span>
<span id="cb10-110"><a href="#cb10-110" aria-hidden="true" tabindex="-1"></a>  <span class="fu">my_theme</span>() <span class="sc">+</span></span>
<span id="cb10-111"><a href="#cb10-111" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;top&quot;</span>)</span>
<span id="cb10-112"><a href="#cb10-112" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb10-113"><a href="#cb10-113" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(</span>
<span id="cb10-114"><a href="#cb10-114" aria-hidden="true" tabindex="-1"></a>  <span class="at">filename =</span> <span class="fu">here</span>(<span class="st">&quot;figs&quot;</span>, <span class="st">&quot;final&quot;</span>, <span class="st">&quot;fig_2_lower.png&quot;</span>),</span>
<span id="cb10-115"><a href="#cb10-115" aria-hidden="true" tabindex="-1"></a>  <span class="at">units =</span> <span class="fu">c</span>(<span class="st">&quot;in&quot;</span>),</span>
<span id="cb10-116"><a href="#cb10-116" aria-hidden="true" tabindex="-1"></a>  <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">5</span>,</span>
<span id="cb10-117"><a href="#cb10-117" aria-hidden="true" tabindex="-1"></a>  <span class="at">dpi =</span> <span class="dv">300</span></span>
<span id="cb10-118"><a href="#cb10-118" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb10-119"><a href="#cb10-119" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb10-120"><a href="#cb10-120" aria-hidden="true" tabindex="-1"></a><span class="co"># Combine the plots vertically</span></span>
<span id="cb10-121"><a href="#cb10-121" aria-hidden="true" tabindex="-1"></a>combined_plot <span class="ot">&lt;-</span> plot_pp_three_green <span class="sc">/</span> plot_me_three_green</span>
<span id="cb10-122"><a href="#cb10-122" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb10-123"><a href="#cb10-123" aria-hidden="true" tabindex="-1"></a><span class="co"># Save the combined plot</span></span>
<span id="cb10-124"><a href="#cb10-124" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(</span>
<span id="cb10-125"><a href="#cb10-125" aria-hidden="true" tabindex="-1"></a>  <span class="at">filename =</span> <span class="fu">here</span>(<span class="st">&quot;figs&quot;</span>, <span class="st">&quot;final&quot;</span>, <span class="st">&quot;figure_2.png&quot;</span>),</span>
<span id="cb10-126"><a href="#cb10-126" aria-hidden="true" tabindex="-1"></a>  <span class="at">plot =</span> combined_plot,</span>
<span id="cb10-127"><a href="#cb10-127" aria-hidden="true" tabindex="-1"></a>  <span class="at">units =</span> <span class="fu">c</span>(<span class="st">&quot;in&quot;</span>),</span>
<span id="cb10-128"><a href="#cb10-128" aria-hidden="true" tabindex="-1"></a>  <span class="at">width =</span> <span class="dv">8</span>,</span>
<span id="cb10-129"><a href="#cb10-129" aria-hidden="true" tabindex="-1"></a>  <span class="at">height =</span> <span class="dv">10</span>,</span>
<span id="cb10-130"><a href="#cb10-130" aria-hidden="true" tabindex="-1"></a>  <span class="at">dpi =</span> <span class="dv">300</span></span>
<span id="cb10-131"><a href="#cb10-131" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb10-132"><a href="#cb10-132" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb10-133"><a href="#cb10-133" aria-hidden="true" tabindex="-1"></a>combined_plot</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<div id="fig-fig_2" class="quarto-float quarto-figure quarto-figure-center anchored">
<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-fig_2-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<img role="img" 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class="img-fluid figure-img" width="768">
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-fig_2-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure 8: Predicted probabilities and marginal effects of voting for a green party (three-way interaction)
</figcaption>
</figure>
</div>
</div>
</div>
</section>
<section id="table-with-predicted-probabilities-of-voting-for-the-greens" class="level2" data-number="3.5">
<h2 data-number="3.5" class="anchored" data-anchor-id="table-with-predicted-probabilities-of-voting-for-the-greens"><span class="header-section-number">3.5</span> Table with predicted probabilities of voting for the greens</h2>
<div class="cell">
<details class="code-fold">
<summary>Show the code</summary>
<div class="sourceCode cell-code" id="cb11"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a>pp_three_green <span class="sc">%&gt;%</span> </span>
<span id="cb11-2"><a href="#cb11-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(cohort_fct<span class="sc">:</span>p.value) <span class="sc">%&gt;%</span> </span>
<span id="cb11-3"><a href="#cb11-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">rename</span>(<span class="at">cohort =</span> cohort_fct) <span class="sc">%&gt;%</span> </span>
<span id="cb11-4"><a href="#cb11-4" aria-hidden="true" tabindex="-1"></a><span class="fu">gt</span>() <span class="sc">%&gt;%</span> </span>
<span id="cb11-5"><a href="#cb11-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">fmt_number</span>(</span>
<span id="cb11-6"><a href="#cb11-6" aria-hidden="true" tabindex="-1"></a>    <span class="at">decimals =</span> <span class="dv">3</span></span>
<span id="cb11-7"><a href="#cb11-7" aria-hidden="true" tabindex="-1"></a>  )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div id="tbl-table_pp_green" class="cell quarto-float quarto-figure quarto-figure-center anchored">
<figure class="quarto-float quarto-float-tbl figure">
<figcaption class="quarto-float-caption-top quarto-float-caption quarto-float-tbl" id="tbl-table_pp_green-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Table 1: Predicted probabilities of voting for the greens (threeway interaction)
</figcaption>
<div aria-describedby="tbl-table_pp_green-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
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padding-top: 0;
padding-bottom: 0;
padding-left: 4px;
padding-right: 4px;
}
#cwgduwotmi .gt_column_spanner_outer:first-child {
padding-left: 0;
}
#cwgduwotmi .gt_column_spanner_outer:last-child {
padding-right: 0;
}
#cwgduwotmi .gt_column_spanner {
border-bottom-style: solid;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
vertical-align: bottom;
padding-top: 5px;
padding-bottom: 5px;
overflow-x: hidden;
display: inline-block;
width: 100%;
}
#cwgduwotmi .gt_spanner_row {
border-bottom-style: hidden;
}
#cwgduwotmi .gt_group_heading {
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
color: #333333;
background-color: #FFFFFF;
font-size: 100%;
font-weight: initial;
text-transform: inherit;
border-top-style: solid;
border-top-width: 2px;
border-top-color: #D3D3D3;
border-bottom-style: solid;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
border-left-style: none;
border-left-width: 1px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 1px;
border-right-color: #D3D3D3;
vertical-align: middle;
text-align: left;
}
#cwgduwotmi .gt_empty_group_heading {
padding: 0.5px;
color: #333333;
background-color: #FFFFFF;
font-size: 100%;
font-weight: initial;
border-top-style: solid;
border-top-width: 2px;
border-top-color: #D3D3D3;
border-bottom-style: solid;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
vertical-align: middle;
}
#cwgduwotmi .gt_from_md > :first-child {
margin-top: 0;
}
#cwgduwotmi .gt_from_md > :last-child {
margin-bottom: 0;
}
#cwgduwotmi .gt_row {
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
margin: 10px;
border-top-style: solid;
border-top-width: 1px;
border-top-color: #D3D3D3;
border-left-style: none;
border-left-width: 1px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 1px;
border-right-color: #D3D3D3;
vertical-align: middle;
overflow-x: hidden;
}
#cwgduwotmi .gt_stub {
color: #333333;
background-color: #FFFFFF;
font-size: 100%;
font-weight: initial;
text-transform: inherit;
border-right-style: solid;
border-right-width: 2px;
border-right-color: #D3D3D3;
padding-left: 5px;
padding-right: 5px;
}
#cwgduwotmi .gt_stub_row_group {
color: #333333;
background-color: #FFFFFF;
font-size: 100%;
font-weight: initial;
text-transform: inherit;
border-right-style: solid;
border-right-width: 2px;
border-right-color: #D3D3D3;
padding-left: 5px;
padding-right: 5px;
vertical-align: top;
}
#cwgduwotmi .gt_row_group_first td {
border-top-width: 2px;
}
#cwgduwotmi .gt_row_group_first th {
border-top-width: 2px;
}
#cwgduwotmi .gt_summary_row {
color: #333333;
background-color: #FFFFFF;
text-transform: inherit;
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
}
#cwgduwotmi .gt_first_summary_row {
border-top-style: solid;
border-top-color: #D3D3D3;
}
#cwgduwotmi .gt_first_summary_row.thick {
border-top-width: 2px;
}
#cwgduwotmi .gt_last_summary_row {
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
border-bottom-style: solid;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
}
#cwgduwotmi .gt_grand_summary_row {
color: #333333;
background-color: #FFFFFF;
text-transform: inherit;
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
}
#cwgduwotmi .gt_first_grand_summary_row {
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
border-top-style: double;
border-top-width: 6px;
border-top-color: #D3D3D3;
}
#cwgduwotmi .gt_last_grand_summary_row_top {
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
border-bottom-style: double;
border-bottom-width: 6px;
border-bottom-color: #D3D3D3;
}
#cwgduwotmi .gt_striped {
background-color: rgba(128, 128, 128, 0.05);
}
#cwgduwotmi .gt_table_body {
border-top-style: solid;
border-top-width: 2px;
border-top-color: #D3D3D3;
border-bottom-style: solid;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
}
#cwgduwotmi .gt_footnotes {
color: #333333;
background-color: #FFFFFF;
border-bottom-style: none;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
border-left-style: none;
border-left-width: 2px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 2px;
border-right-color: #D3D3D3;
}
#cwgduwotmi .gt_footnote {
margin: 0px;
font-size: 90%;
padding-top: 4px;
padding-bottom: 4px;
padding-left: 5px;
padding-right: 5px;
}
#cwgduwotmi .gt_sourcenotes {
color: #333333;
background-color: #FFFFFF;
border-bottom-style: none;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
border-left-style: none;
border-left-width: 2px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 2px;
border-right-color: #D3D3D3;
}
#cwgduwotmi .gt_sourcenote {
font-size: 90%;
padding-top: 4px;
padding-bottom: 4px;
padding-left: 5px;
padding-right: 5px;
}
#cwgduwotmi .gt_left {
text-align: left;
}
#cwgduwotmi .gt_center {
text-align: center;
}
#cwgduwotmi .gt_right {
text-align: right;
font-variant-numeric: tabular-nums;
}
#cwgduwotmi .gt_font_normal {
font-weight: normal;
}
#cwgduwotmi .gt_font_bold {
font-weight: bold;
}
#cwgduwotmi .gt_font_italic {
font-style: italic;
}
#cwgduwotmi .gt_super {
font-size: 65%;
}
#cwgduwotmi .gt_footnote_marks {
font-size: 75%;
vertical-align: 0.4em;
position: initial;
}
#cwgduwotmi .gt_asterisk {
font-size: 100%;
vertical-align: 0;
}
#cwgduwotmi .gt_indent_1 {
text-indent: 5px;
}
#cwgduwotmi .gt_indent_2 {
text-indent: 10px;
}
#cwgduwotmi .gt_indent_3 {
text-indent: 15px;
}
#cwgduwotmi .gt_indent_4 {
text-indent: 20px;
}
#cwgduwotmi .gt_indent_5 {
text-indent: 25px;
}
#cwgduwotmi .katex-display {
display: inline-flex !important;
margin-bottom: 0.75em !important;
}
#cwgduwotmi div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
height: 0px !important;
}
</style>

<table class="gt_table do-not-create-environment cell caption-top table table-sm table-striped small" data-quarto-postprocess="true" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
<thead>
<tr class="header gt_col_headings">
<th id="cohort" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col">cohort</th>
<th id="edu_fct" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col">edu_fct</th>
<th id="estimate" class="gt_col_heading gt_columns_bottom_border gt_right" data-quarto-table-cell-role="th" scope="col">estimate</th>
<th id="std.error" class="gt_col_heading gt_columns_bottom_border gt_right" data-quarto-table-cell-role="th" scope="col">std.error</th>
<th id="statistic" class="gt_col_heading gt_columns_bottom_border gt_right" data-quarto-table-cell-role="th" scope="col">statistic</th>
<th id="p.value" class="gt_col_heading gt_columns_bottom_border gt_right" data-quarto-table-cell-role="th" scope="col">p.value</th>
</tr>
</thead>
<tbody class="gt_table_body">
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">interwar</td>
<td class="gt_row gt_center" headers="edu_fct">(below) lower secondary</td>
<td class="gt_row gt_right" headers="estimate">0.008</td>
<td class="gt_row gt_right" headers="std.error">0.004</td>
<td class="gt_row gt_right" headers="statistic">1.998</td>
<td class="gt_row gt_right" headers="p.value">0.046</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">interwar</td>
<td class="gt_row gt_center" headers="edu_fct">upper secondary</td>
<td class="gt_row gt_right" headers="estimate">0.017</td>
<td class="gt_row gt_right" headers="std.error">0.008</td>
<td class="gt_row gt_right" headers="statistic">2.173</td>
<td class="gt_row gt_right" headers="p.value">0.030</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">interwar</td>
<td class="gt_row gt_center" headers="edu_fct">tertiary</td>
<td class="gt_row gt_right" headers="estimate">0.025</td>
<td class="gt_row gt_right" headers="std.error">0.011</td>
<td class="gt_row gt_right" headers="statistic">2.223</td>
<td class="gt_row gt_right" headers="p.value">0.026</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">post-war</td>
<td class="gt_row gt_center" headers="edu_fct">(below) lower secondary</td>
<td class="gt_row gt_right" headers="estimate">0.011</td>
<td class="gt_row gt_right" headers="std.error">0.002</td>
<td class="gt_row gt_right" headers="statistic">4.518</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">post-war</td>
<td class="gt_row gt_center" headers="edu_fct">upper secondary</td>
<td class="gt_row gt_right" headers="estimate">0.029</td>
<td class="gt_row gt_right" headers="std.error">0.004</td>
<td class="gt_row gt_right" headers="statistic">8.094</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">post-war</td>
<td class="gt_row gt_center" headers="edu_fct">tertiary</td>
<td class="gt_row gt_right" headers="estimate">0.045</td>
<td class="gt_row gt_right" headers="std.error">0.005</td>
<td class="gt_row gt_right" headers="statistic">9.530</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">60-70s</td>
<td class="gt_row gt_center" headers="edu_fct">(below) lower secondary</td>
<td class="gt_row gt_right" headers="estimate">0.028</td>
<td class="gt_row gt_right" headers="std.error">0.004</td>
<td class="gt_row gt_right" headers="statistic">7.780</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">60-70s</td>
<td class="gt_row gt_center" headers="edu_fct">upper secondary</td>
<td class="gt_row gt_right" headers="estimate">0.039</td>
<td class="gt_row gt_right" headers="std.error">0.003</td>
<td class="gt_row gt_right" headers="statistic">12.378</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">60-70s</td>
<td class="gt_row gt_center" headers="edu_fct">tertiary</td>
<td class="gt_row gt_right" headers="estimate">0.076</td>
<td class="gt_row gt_right" headers="std.error">0.005</td>
<td class="gt_row gt_right" headers="statistic">15.372</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">80s</td>
<td class="gt_row gt_center" headers="edu_fct">(below) lower secondary</td>
<td class="gt_row gt_right" headers="estimate">0.038</td>
<td class="gt_row gt_right" headers="std.error">0.006</td>
<td class="gt_row gt_right" headers="statistic">6.409</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">80s</td>
<td class="gt_row gt_center" headers="edu_fct">upper secondary</td>
<td class="gt_row gt_right" headers="estimate">0.051</td>
<td class="gt_row gt_right" headers="std.error">0.004</td>
<td class="gt_row gt_right" headers="statistic">12.796</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">80s</td>
<td class="gt_row gt_center" headers="edu_fct">tertiary</td>
<td class="gt_row gt_right" headers="estimate">0.109</td>
<td class="gt_row gt_right" headers="std.error">0.007</td>
<td class="gt_row gt_right" headers="statistic">16.538</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">90s</td>
<td class="gt_row gt_center" headers="edu_fct">(below) lower secondary</td>
<td class="gt_row gt_right" headers="estimate">0.055</td>
<td class="gt_row gt_right" headers="std.error">0.009</td>
<td class="gt_row gt_right" headers="statistic">5.896</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">90s</td>
<td class="gt_row gt_center" headers="edu_fct">upper secondary</td>
<td class="gt_row gt_right" headers="estimate">0.063</td>
<td class="gt_row gt_right" headers="std.error">0.005</td>
<td class="gt_row gt_right" headers="statistic">12.619</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">90s</td>
<td class="gt_row gt_center" headers="edu_fct">tertiary</td>
<td class="gt_row gt_right" headers="estimate">0.105</td>
<td class="gt_row gt_right" headers="std.error">0.007</td>
<td class="gt_row gt_right" headers="statistic">15.895</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">millennial</td>
<td class="gt_row gt_center" headers="edu_fct">(below) lower secondary</td>
<td class="gt_row gt_right" headers="estimate">0.049</td>
<td class="gt_row gt_right" headers="std.error">0.016</td>
<td class="gt_row gt_right" headers="statistic">3.172</td>
<td class="gt_row gt_right" headers="p.value">0.002</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">millennial</td>
<td class="gt_row gt_center" headers="edu_fct">upper secondary</td>
<td class="gt_row gt_right" headers="estimate">0.073</td>
<td class="gt_row gt_right" headers="std.error">0.008</td>
<td class="gt_row gt_right" headers="statistic">9.631</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">millennial</td>
<td class="gt_row gt_center" headers="edu_fct">tertiary</td>
<td class="gt_row gt_right" headers="estimate">0.114</td>
<td class="gt_row gt_right" headers="std.error">0.009</td>
<td class="gt_row gt_right" headers="statistic">13.121</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">interwar</td>
<td class="gt_row gt_center" headers="edu_fct">(below) lower secondary</td>
<td class="gt_row gt_right" headers="estimate">0.020</td>
<td class="gt_row gt_right" headers="std.error">0.006</td>
<td class="gt_row gt_right" headers="statistic">3.250</td>
<td class="gt_row gt_right" headers="p.value">0.001</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">interwar</td>
<td class="gt_row gt_center" headers="edu_fct">upper secondary</td>
<td class="gt_row gt_right" headers="estimate">0.012</td>
<td class="gt_row gt_right" headers="std.error">0.006</td>
<td class="gt_row gt_right" headers="statistic">1.967</td>
<td class="gt_row gt_right" headers="p.value">0.049</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">interwar</td>
<td class="gt_row gt_center" headers="edu_fct">tertiary</td>
<td class="gt_row gt_right" headers="estimate">0.043</td>
<td class="gt_row gt_right" headers="std.error">0.019</td>
<td class="gt_row gt_right" headers="statistic">2.280</td>
<td class="gt_row gt_right" headers="p.value">0.023</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">post-war</td>
<td class="gt_row gt_center" headers="edu_fct">(below) lower secondary</td>
<td class="gt_row gt_right" headers="estimate">0.028</td>
<td class="gt_row gt_right" headers="std.error">0.004</td>
<td class="gt_row gt_right" headers="statistic">6.642</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">post-war</td>
<td class="gt_row gt_center" headers="edu_fct">upper secondary</td>
<td class="gt_row gt_right" headers="estimate">0.050</td>
<td class="gt_row gt_right" headers="std.error">0.005</td>
<td class="gt_row gt_right" headers="statistic">9.870</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">post-war</td>
<td class="gt_row gt_center" headers="edu_fct">tertiary</td>
<td class="gt_row gt_right" headers="estimate">0.082</td>
<td class="gt_row gt_right" headers="std.error">0.008</td>
<td class="gt_row gt_right" headers="statistic">10.306</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">60-70s</td>
<td class="gt_row gt_center" headers="edu_fct">(below) lower secondary</td>
<td class="gt_row gt_right" headers="estimate">0.043</td>
<td class="gt_row gt_right" headers="std.error">0.004</td>
<td class="gt_row gt_right" headers="statistic">10.185</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">60-70s</td>
<td class="gt_row gt_center" headers="edu_fct">upper secondary</td>
<td class="gt_row gt_right" headers="estimate">0.060</td>
<td class="gt_row gt_right" headers="std.error">0.004</td>
<td class="gt_row gt_right" headers="statistic">14.436</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">60-70s</td>
<td class="gt_row gt_center" headers="edu_fct">tertiary</td>
<td class="gt_row gt_right" headers="estimate">0.120</td>
<td class="gt_row gt_right" headers="std.error">0.007</td>
<td class="gt_row gt_right" headers="statistic">17.306</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">80s</td>
<td class="gt_row gt_center" headers="edu_fct">(below) lower secondary</td>
<td class="gt_row gt_right" headers="estimate">0.057</td>
<td class="gt_row gt_right" headers="std.error">0.006</td>
<td class="gt_row gt_right" headers="statistic">8.896</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">80s</td>
<td class="gt_row gt_center" headers="edu_fct">upper secondary</td>
<td class="gt_row gt_right" headers="estimate">0.079</td>
<td class="gt_row gt_right" headers="std.error">0.005</td>
<td class="gt_row gt_right" headers="statistic">15.470</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">80s</td>
<td class="gt_row gt_center" headers="edu_fct">tertiary</td>
<td class="gt_row gt_right" headers="estimate">0.151</td>
<td class="gt_row gt_right" headers="std.error">0.008</td>
<td class="gt_row gt_right" headers="statistic">18.757</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">90s</td>
<td class="gt_row gt_center" headers="edu_fct">(below) lower secondary</td>
<td class="gt_row gt_right" headers="estimate">0.052</td>
<td class="gt_row gt_right" headers="std.error">0.008</td>
<td class="gt_row gt_right" headers="statistic">6.540</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">90s</td>
<td class="gt_row gt_center" headers="edu_fct">upper secondary</td>
<td class="gt_row gt_right" headers="estimate">0.080</td>
<td class="gt_row gt_right" headers="std.error">0.006</td>
<td class="gt_row gt_right" headers="statistic">14.190</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">90s</td>
<td class="gt_row gt_center" headers="edu_fct">tertiary</td>
<td class="gt_row gt_right" headers="estimate">0.157</td>
<td class="gt_row gt_right" headers="std.error">0.008</td>
<td class="gt_row gt_right" headers="statistic">18.851</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">millennial</td>
<td class="gt_row gt_center" headers="edu_fct">(below) lower secondary</td>
<td class="gt_row gt_right" headers="estimate">0.067</td>
<td class="gt_row gt_right" headers="std.error">0.017</td>
<td class="gt_row gt_right" headers="statistic">3.960</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">millennial</td>
<td class="gt_row gt_center" headers="edu_fct">upper secondary</td>
<td class="gt_row gt_right" headers="estimate">0.092</td>
<td class="gt_row gt_right" headers="std.error">0.009</td>
<td class="gt_row gt_right" headers="statistic">10.759</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">millennial</td>
<td class="gt_row gt_center" headers="edu_fct">tertiary</td>
<td class="gt_row gt_right" headers="estimate">0.178</td>
<td class="gt_row gt_right" headers="std.error">0.011</td>
<td class="gt_row gt_right" headers="statistic">16.516</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
</tbody>
</table>

</div>
</div>
</div>
</figure>
</div>
</div>
</section>
<section id="multilevel-model-voting-for-the-greens-education-ess-round-gender-three-way-interaction" class="level2" data-number="3.6">
<h2 data-number="3.6" class="anchored" data-anchor-id="multilevel-model-voting-for-the-greens-education-ess-round-gender-three-way-interaction"><span class="header-section-number">3.6</span> Multilevel model: Voting for the greens – education, ESS round &amp; gender (three-way interaction)</h2>
<div class="cell">
<details class="code-fold">
<summary>Show the code</summary>
<div class="sourceCode cell-code" id="cb12"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a><span class="co"># predicted probabilities: by period</span></span>
<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a>pp_three_green_period <span class="ot">&lt;-</span> <span class="fu">predictions</span>(three_green,</span>
<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a>  <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&quot;gndr_fct&quot;</span>, <span class="st">&quot;essround&quot;</span>, <span class="st">&quot;edu_fct&quot;</span>),</span>
<span id="cb12-4"><a href="#cb12-4" aria-hidden="true" tabindex="-1"></a>  <span class="at">newdata =</span> <span class="fu">datagrid</span>(</span>
<span id="cb12-5"><a href="#cb12-5" aria-hidden="true" tabindex="-1"></a>    <span class="at">essround =</span> <span class="fu">c</span>(<span class="st">&quot;1&quot;</span>, <span class="st">&quot;2&quot;</span>, <span class="st">&quot;3&quot;</span>, <span class="st">&quot;4&quot;</span>, <span class="st">&quot;5&quot;</span>, <span class="st">&quot;6&quot;</span>, <span class="st">&quot;7&quot;</span>, <span class="st">&quot;8&quot;</span>, <span class="st">&quot;9&quot;</span>, <span class="st">&quot;10&quot;</span>),</span>
<span id="cb12-6"><a href="#cb12-6" aria-hidden="true" tabindex="-1"></a>    <span class="at">edu_fct =</span> <span class="fu">c</span>(</span>
<span id="cb12-7"><a href="#cb12-7" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;(below) lower secondary&quot;</span>,</span>
<span id="cb12-8"><a href="#cb12-8" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;upper secondary&quot;</span>,</span>
<span id="cb12-9"><a href="#cb12-9" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;tertiary&quot;</span></span>
<span id="cb12-10"><a href="#cb12-10" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb12-11"><a href="#cb12-11" aria-hidden="true" tabindex="-1"></a>    <span class="at">grid_type =</span> <span class="st">&quot;counterfactual&quot;</span></span>
<span id="cb12-12"><a href="#cb12-12" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb12-13"><a href="#cb12-13" aria-hidden="true" tabindex="-1"></a>    <span class="at">re.form =</span> <span class="cn">NA</span></span>
<span id="cb12-14"><a href="#cb12-14" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb12-15"><a href="#cb12-15" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb12-16"><a href="#cb12-16" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(</span>
<span id="cb12-17"><a href="#cb12-17" aria-hidden="true" tabindex="-1"></a>  <span class="at">data =</span> pp_three_green_period,</span>
<span id="cb12-18"><a href="#cb12-18" aria-hidden="true" tabindex="-1"></a>  <span class="fu">aes</span>(</span>
<span id="cb12-19"><a href="#cb12-19" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> essround,</span>
<span id="cb12-20"><a href="#cb12-20" aria-hidden="true" tabindex="-1"></a>    <span class="at">y =</span> estimate,</span>
<span id="cb12-21"><a href="#cb12-21" aria-hidden="true" tabindex="-1"></a>    <span class="at">ymin =</span> conf.low,</span>
<span id="cb12-22"><a href="#cb12-22" aria-hidden="true" tabindex="-1"></a>    <span class="at">ymax =</span> conf.high,</span>
<span id="cb12-23"><a href="#cb12-23" aria-hidden="true" tabindex="-1"></a>    <span class="at">color =</span> edu_fct</span>
<span id="cb12-24"><a href="#cb12-24" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb12-25"><a href="#cb12-25" aria-hidden="true" tabindex="-1"></a>) <span class="sc">+</span></span>
<span id="cb12-26"><a href="#cb12-26" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_pointrange</span>(<span class="at">position =</span> <span class="fu">position_dodge</span>(<span class="at">width =</span> <span class="fu">c</span>(<span class="fl">0.2</span>))) <span class="sc">+</span></span>
<span id="cb12-27"><a href="#cb12-27" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb12-28"><a href="#cb12-28" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> <span class="cn">NULL</span>, <span class="at">y =</span> <span class="st">&quot;Predicted probability&quot;</span>,</span>
<span id="cb12-29"><a href="#cb12-29" aria-hidden="true" tabindex="-1"></a>    <span class="at">color =</span> <span class="cn">NULL</span></span>
<span id="cb12-30"><a href="#cb12-30" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb12-31"><a href="#cb12-31" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_viridis_d</span>(<span class="at">option =</span> <span class="st">&quot;plasma&quot;</span>, <span class="at">end =</span> .<span class="dv">8</span>) <span class="sc">+</span></span>
<span id="cb12-32"><a href="#cb12-32" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_light</span>() <span class="sc">+</span></span>
<span id="cb12-33"><a href="#cb12-33" aria-hidden="true" tabindex="-1"></a>  <span class="fu">my_theme</span>() <span class="sc">+</span></span>
<span id="cb12-34"><a href="#cb12-34" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;top&quot;</span>) <span class="sc">+</span></span>
<span id="cb12-35"><a href="#cb12-35" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>gndr_fct)</span>
<span id="cb12-36"><a href="#cb12-36" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb12-37"><a href="#cb12-37" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(</span>
<span id="cb12-38"><a href="#cb12-38" aria-hidden="true" tabindex="-1"></a>  <span class="at">filename =</span> <span class="fu">here</span>(<span class="st">&quot;figs&quot;</span>, <span class="st">&quot;final&quot;</span>, <span class="st">&quot;appendix_fig_d3.png&quot;</span>),</span>
<span id="cb12-39"><a href="#cb12-39" aria-hidden="true" tabindex="-1"></a>  <span class="at">units =</span> <span class="fu">c</span>(<span class="st">&quot;in&quot;</span>),</span>
<span id="cb12-40"><a href="#cb12-40" aria-hidden="true" tabindex="-1"></a>  <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">5</span>,</span>
<span id="cb12-41"><a href="#cb12-41" aria-hidden="true" tabindex="-1"></a>  <span class="at">dpi =</span> <span class="dv">300</span></span>
<span id="cb12-42"><a href="#cb12-42" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<div id="fig-fig_d3" class="quarto-float quarto-figure quarto-figure-center anchored">
<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-fig_d3-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<img role="img" 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6aICgMBOKKyglQYMkVUGAggRIZMERUGAnBEZQWpMGSKqDAQQIgMmSIqDATgiMoKUmHIFFFhIIAQGTJFVBgIwBGVFaTCkCmiwkAAITJkiqgwEIAjKitIhSFTRIWBAEJkyBRRYSAAR1RWkApDpogKAwGEyJAposJAAI6orCAVhkwRFQYCCJEhU0SFgQAcUVlBKgyZIioMBBAiQ6aICgMBOKKyglQYMkVUGAggRIZMERUGAnBEZQWpMGSKqDAQQIgMmSIqDATgiMoKUmHIFFFhIIAQGTJFVBgIwBGVFaTCkCmiwkAAITJkiqgwEIAjKitIhSFTRIWBAEJkyBRRYSAAR1RWkApDpogKAwGEyJAposJAAI6orCAVhkwRFQYCCJEhU0SFgQAcUVlBKgyZIioMBBAiQ6aICgMBOKKyglQYMkVUGAggRIZMERUGAnBEZQWpMGSKqDAQQIgMmSIqDATgiMoKUmHIFFFhIIAQGTJFVBgIwBGVFaTCkCmiwkAAITJkiqgwEIAjKitIhSFTRIWBAEJkyBRRYSAAR1RWkApDpogKAwGEyJAposJAAI6orCAVhkwRFQYCCJEhU0SFgQAcUVlBKgyZIioMBBAiQ6aICgMBOKKyglQYMkVUGAggRIZMERUGAnBEZQWpMGSKqDAQQIgMmSIqDATgiMoKUmHIFFFhIIAQGTJFVBgIwBGVFaTCkCmiwkAAITJkiqgwrIqsVh2mQwDhMmSKqDAQQIgMmSIqDATgiMoKUmHIFFFhIIAQGTJFVBgIwBGVFaTCkCmiwkAAITJkiqgwEIAjKitIhSFTRIWBAEJkyBRRYSAAR1RWkApDpogKAwGEyJAposJAAI6orCAVhkwRFQYCCJEhU0SFgQAcUVlBKgyZIioMBBAiQ6aICgMBOKKyglQYMkVUGAggRIZMERUGAnBEZQWpMGSKqDAQQIgMmSIqDATgiMoKUmHIFFFhIIAQGTJFVBgIwBGVFaTCkCmiwkAAITJkiqgwEIAjKitIhSFTRIWBAEJkyBRRYSAAR1RWkApDpogKAwGEyJAposJAAI6orCAVhkwRFQYCCJEhU0SFgQAcUVlBKgyZIioMBBAiQ6aICgMBOKKyglQYMkVUGAggRIZMERUGAnBEZQWpMGSKqDAQQIgMmSIqDATgiMoKUmHIFFFhIIAQGTJFVBgIwBGVFaTCkCmiwkAAITJkiqgwghPA57dJkrz6UPvtr9+/PPh1+1QpApgWEhMDAYTIkCmiwghNAOskz8X7w98uil8nr49NlVfo+kQqK0iFIVNEhYEAQmTIFFFhBCaA+0Xy/EP6+Sa5+Pbwt89+SLNfJ+/dUxUVuj6TygpSYcgUUWEggBAZMkVUGIEJYJk8/7T5n8eb5EXlt3fJkx/T4tf5n9unKip0fSaVFaTCkCmiwkAAITJkiqgwwhLAw3XxEj9dl0N+nv1Av85f87dPVVbo+lQqK0iFIVNEhYEAQmTIFFFhhCWA3Yi+G+MPc7/IBHBsKgQwJSQmBgIIkSFTRIURlgDuimM7+Yv+1y1/L94BHJsKAUwJiYmBAEJkyBRRYYQlgGVlaG8c3s//nr32PzYVApgSEhMDAYTIkCmiwghNAC8aP1Vyv8h/e2wqBDAlJCYGAgiRIVNEhRGTADYv+POD/21T/WmbfxISXBoCmLoQIQdZrTpMNK4AHrcfA+AdwAgMmSIqDN4BhMiQKaLCiOcdQDb+vz45FQKYEhITAwGEyJAposIITQDO07v78Z+TwGMwZIqoMBBAiAyZIiqMsATgvsDz4U2SvDs5FQKYFhITAwGEyJAposIISwDOj3h9vq7c+Y0Pgo3AkCmiwkAAITJkiqgwwhKA6yYPm99X7vvGrSBGYMgUUWEggBAZMkVUGGEJwHGbt8fafT+5GZw9Q6aICgMBhMiQKaLCCEwA7Td6vksOD/VwO2h7hkwRFQYCCJEhU0SFEZgA0o8HX/Wyzkf+h+tkn9fNqQ4qdH0ilRWkwpAposJAACEyZIqoMEITwOGXPRYCuF/UBcBXQpozZIqoMBBAiAyZIiqM4ATgGQQwJSQmBgIIkSFTRIWBABxRWUEqDJkiKgwEECJDpogKAwE4orKCVBgyRVQYCCBEhkwRFQYCcERlBakwZIqoMBBAiAyZIioMBOCIygpSYcgUUWEggBAZMkVUGAjAEZUVpMKQKaLCQAAhMmSKqDAQgCMqK0iFIVNEhYEAQmTIFFFhIABHVFaQCkOmiAoDAYTIkCmiwkAAjqisIBWGTBEVBgIIkSFTRIWBABxRWUEqDJkiKgwEECJDpogKAwE4orKCVBgyRVQYCCBEhkwRFQYCcERlBakwZIqoMBBAiAyZIioMBOCIygpSYcgUUWEggBAZMkVUGAjAEZUVpMKQKaLCQAAhMmSKqDAQgCMqK0iFIVNEhYEAQmTIFFFhIABHVFaQCkOmiAoDAYTIkCmiwkAAjqisIBWGTBEVBgIIkSFTRIWBABxRWUEqDJkiKgwEECJDpogKAwE4orKCVBgyRVQYCCBEhkwRFQYCcERlBakwZIqoMBBAiAyZIioMBOCIygpSYcgUUWEggBAZMkVUGAjAEZUVpMKQKaLCQAAhMmSKqDAQgCMqK0iFIVNEhYEAQmTIFFFhIABHVFaQCkOmiAoDAYTIkCmiwkAAjqisIBWGTBEVBgIIkSFTRIWBABxRWUEqDJkiKgwEECJDpogKAwE4orKCVBgyRVQYCCBEhkwRFQYCcERlBakwZIqoMBBAiAyZIioMBOCIygpSYcgUUWEggBAZMkVUGAjAEZUVpMKQKaLCQAAhMmSKqDAQgCMqK0iFIVNEhYEAQmTIFFFhIABHVFaQCkOmiAoDAYTIkCmiwkAAjqisIBWGTBEVBgIIkSFTRIWBABxRWUEqDJkiKgwEECJDpogKAwE4orKCVBgyRVQYCCBEhkwRFQYCcERlBakwZIqoMBBAiAyZIioMBOCIygpSYcgUUWEggBAZMkVUGAjAEZUVpMKQKaLCQAAhMmSKqDAQgCMqK0iFIVNEhYEAQmTIFFFhIABHVFaQCkOmiAoDAYTIkCmiwkAAjqisIBWGTBEVBgIIkSFTRIWBABxRWUEqDJkiKgwEECJDpogKAwE4orKCVBgyRVQYCCBEhkwRFQYCcERlBakwZIqoMBBAiAyZIioMBOCIygpSYcgUUWEggBAZMkVUGAjAEZUVpMKQKaLCQAAhMmSKqDAQgCMqK0iFIVNEhYEAQmTIFFFhIABHVFaQCkOmiAoDAYTIkCmiwkAAjqisIBWGTBEVBgIIkSFTRIWBABxRWUEqDJkiKgwEECJDpogKAwE4orKCVBgyRVQYCCBEhkwRFQYCcERlBakwZIqoMBBAiAyZIioMBOCIygpSYcgUUWEggBAZMkVUGAjAEZUVpMKQKaLCQAAhMmSKqDAQgCMqK0iFIVNEhYEAQmTIFFFhIABHVFaQCkOmiAoDAYTIkCmiwkAAjqisIBWGTBEVBgIIkSFTRIWBABxRWUEqDJkiKgwEECJDpogKAwE4orKCVBgyRVQYCCBEhkwRFQYCcERlBakwZIqoMBBAiAyZIioMBOCIygpSYcgUUWEggBAZMkVUGAjAEZUVpMKQKaLCQAAhMmSKZIxOg+bIPRCAI0obigJDpogKAwGEyJApggBSBBASQ6aICgMBhMiQKYIAUgQQEkOmiAoDAYTIkCmCAFIEEBJDpogKAwGEyJApggBSBBASQ6aICgMBhMiQKYIAUgQQEkOmiAoDAYTIkCmCAFIEEBJDpogKAwGEyJApggBSBBASQ6aICgMBhMiQKYIAUgQQEkOmiAoDAYTIkCmCAFIEEBJDpogKAwGEyJApggBSBBASQ6aICgMBhMiQKYIAUgQQEkOmiAoDAYTIkCmCAFIEEBJDpogKAwGEyJApggBSBBASQ6aICgMBhMiQKYIAUgQQEkOmiAoDAYTIkCmCAFIEEBJDpogKAwGEyJApggBSBBASQ6aICgMBhMiQKRKZAFarLjODAMJlyBRRYSCAEBkyRaISwGqbE9MhgHAZMkVUGAggRIZMkZgEsFp1NAACCJchU0SFgQBCZMgUiUgAq1VXAyCAcBkyRVQYCCBEhkyReASwWnU2AAIIlyFTRIWBAEJkyBRBACkCCIkhU0SFgQBCZMgUiUYAq3qOTIsAwmXIFFFhIIAQGTJFEECKAEJiyBRRYSCAEBkyRRBAigBCYsgUUWEggBAZMkUQQIoAQmLIFFFhIIAQGTJFohEAJ4HnwZAposJAACEyZIoggBQBhMSQKaLCQAAhMmSKIIAUAYTEkCmiwkAAITJkisQjAD4JPAuGTBEVBgIIkSFTJCIBcC+gOTBkiqgwEECIDJkiMQmAu4HOgCFTRIWBAEJkyBSJSgB8H0D8DJkiKgwEECJDpkhkAuAbwWJnyBRRYSCAEBkyRRBAigBCYsgUUWEggBAZMkUQQIoAQmLIFFFhIIAQGTJFEECKAEJiyBRRYSCAEBkyRRBAigBCYsgUUWEggBAZMkUQQIoAQmLIFFFhIIAQGTJFEECKAEJiyBRRYSCAEBkyRRBAigBCYsgUUWEggBAZMkUQQIoAQmLIFFFhIIAQGTJFEECKAEJiyBRRYSCAEBkyRRBAigBCYsgUUWEggBAZMkUQQIoAQmLIFFFhIIAQGTJFEECKAEJiyBRRYSCAEBkyRRBAigBCYsgUUWEggBAZMkUQQIoAQmLIFFFhIIAQGTJFEECKAEJiyBRRYSCAEBkyRRBAigBCYsgUUWEggBAZMkUQQIoAQmLIFFFhIIAQGTJFEECKAEJiyBRRYSCAEBkyRRBAigBCYsgUUWEggBAZMkWUBODbAwHMgCFTRIWBAEJkyBRBACkCCIkhU0SFgQBCZMgUQQApAgiJIVNEhYEAQmTIFEEAKQIIiSFTRIWBAEJkyBRBACkCCIkhU0SFgQBCZMgUQQApAgiJIVNEhYEAQmTIFEEAKQIIiSFTRIWBAEJkyBRBACkCCIkhU0SFgQBCZMgUQQApAgiJIVNEhYEAQmTIFEEAKQIIiSFTRIWBAEJkyBRBACkCCIkhU0SFgQBCZMgUQQApAgiJIVNEhYEAQmTIFEEAKQIIiSFTRIWBAEJkyBRBACkCCIkhU0SFgQBCZMgUQQApAgiJIVNEhYEAQmTIFEEAKQIIiSFTRIWBAEJkyBRBACkCCIkhU0SFgQBCZMgUsRLA2QZvCwYCCJchU0SFgQBCZMgUQQApAgiJIVNEhYEAQmTIFEEAKQIIiSFTRIWBAEJkyBRBACkCCIkhU0SFgQBCZMgUQQApAgiJIVNEhYEAQmTIFEEAKQIIiSFTRIWBAEJkyBRBACkCCIkhU0SFgQBCZMgUQQApAgiJIVNEhYEAQmTIFEEAKQIIiSFTRIWBAEJkyBRBACkCCIkhU0SFgQBCZMgUQQApAgiJIVNEhYEAQmTIFEEAKQIIiSFTRIWBAEJkyBRBACkCCIkhU0SFgQBCZMgUQQApAgiJIVNEhYEAQmRYjXcWPRAAAgiHIVNEhYEAQmQggBYIAmiJyharwpAposJAACEyEEALBAG0RGWLVWHIFFFhIIAQGQigBYIAWqKyxaowZIqoMBBAiAwE0AJBAC1R2WJVGDJFVBgIIEQGAmiBIICWqGyxKgyZIioMBBAiAwG0QBBAS1S2WBWGTBEVxqwEIDNmWkBkZgYBDHiuz2+TJHn1oeUvjzdPfix/vEvKvGhU6Po8KlusCkOmiAoDAfRkWPQwgcjMDALo/1TrYmC/eN/8012yE8ASAVgzZIqoMBBAT4ZFDxOIzMwggN7PdL9Inn9IP98kF9/W/7R51b8VwMN188/bCl2fSWWLVWHIFFFhIICeDIseJhCZmUEAvZ9pmTz/lGZHe+qv7R+/S/YCuF/kU7VW6PpMKlusCkOmiAoDAfRkWPQwgcjMDALo+0QP10lx7Ge9P9yT56c3SXK1+926eehnV6HrU6lssSoMmSIqDATQk2HRwwQiMzMIoO8T7cb9nQnS7T8v3u2lcJe0nCIoK3R9KpUtVoUhU0SFgQB6Mix6mEBkZgYB9H2iu6Q8tvN4k7yu/P7h+stPlXcFyyf/+vUiSV790FKh61OpbLEqDJkiKgwE0JNh0cMEIjMzCKDvEy0rAjg4yvNLevj2oMzrBgEBTAmJiYEAejIsephAZGYGAfR9ouVu2F82D/PvBHC/SC6++pT++l2LARDAlJCYGAigJ8OihwlEZmYQQN8n6iaAdVKeA1juTxX/aZt/EhJcGgI48/OvVmd+wjEjMzMWRYJinEkAuzxcN94C8A5gSkhMDN4B9GRY9DCByMwM7wD6PlFPATQ/LoAAJoXExEAAPRkWPUwgMjODAPo+kfMkcJamAFo0gcv+vdoAACAASURBVACmhMTEQAA9GRY9TCAyM4MA+j6R6zLQPAhgTIZMERUGAujJsOhhApGZGQTQ94lcHwQ7/OMuLZpAAFNCYmIggJ4Mix4mEJmZQQB9n8h5K4iD3+0OFN0vGppAAFNCYmIggJ4Mix4mEJWZWa08m0QqgF+OEJw3g0sPLgMtbga6mapxUzgEMCUkJgYC6MkwecHrHRkBrLbx6xGfAB6un37lupfnsdtB7wWwGfgv3qXp5zdJ8zgRApgSEhMDAfRkIIBqVit/A8QqgOwrv75xID4efCHMujrE7w8LZUN/PtW7ZoUONfOojDMqDJkiKgwE0JOBACpZrQwMELEANvmi7Wsfa18J6RBA+vj9y83w30ZAAFNCYmIggJ4MBLDPamVhgDgF8Pj9oryV28VXx04HDAsCmBISEwMB9GQggH0QQB7XVUC/7hzw9J3zdMCwIIApITExEEBPBgLYZVXP8B5RCiDNHPCydIDzdMCgIIApITExEEBPBgLYBQEUOf45gMetA565vuF9QBDAlJCYGAigJwMB7IIAihwVwG78T9qu+RwaBDAlJCYGAujJQAC7IIAibgHsR/+L7HSA8zveewcBTAmJiYEAejIQwD6cBM7jEMD++H92JefjMmm578PQIIApITExEEBPBgLYBwHkOfo5gN2Xut8vEMD0DJkiKgwE0JOBAPZBAHmOCeDZN5/2v0AAkzNkiqgwEEBPBgKoxGL8j1gAB3cDenj7G/fNgfoGAUwJiYmBAHoyEEA1BuN/rAL4DyN8AHhfoeuEKuOMCkOmiAoDAfRkIIADhv/4H6kAHv/r7/+wfcF///bll37VGhW6TqgyzqgwZIqoMBBATwYCaPTg+wBaUj3kv/m5cUd/vyCAKSExMRBATwYCqDP4RrC2iaoCML0AqKjQdUKVcUaFIVNEhYEAejIQQJ2BAOoTLJNGEIAGQ6aICgMB9GQggDoDAdQnuF80BMAhIA2GTBEVBgLoyUAAdQYCaEzRfAvQ+FJHvyCAKSExMRBATwYCqDMQQGOK+7dX2W0gnl4VefWH9q8FGx4EMCUkJgYC6MlAAHUGAmibyPSDv80KXSdUGWdUGDJFVBgIoCcDAdQZCKBtournAOyDAKaExMRAAD0ZCKDOQACezzsgCGBKSEwMBNCTgQDqDATg+bwDggCmhMTEQAA9GQigzkAAtb8/vL169ePmP9W84nMAEgyZIioMBNCTgQDqDARQ+3t+/nf/fQB8EEyIIVNEhRGMAFSGiKgEILNAVGYGAUTOkCmiwkAAPRkIoMZAAAggHIZMERUGAujJQAA1BgLgJHA4DJkiKgwE0JOBAGoMBIAAwmHIFFFhIICeDARQYyAABBAOQ6aICgMB9GQggBoDASCAcBgyRVQYCKAnAwHUGAig5XMAjfA5AA2GTBEVBgLoyUAANQYCaLkKiC+EEWXIFFFhIICeDARQYyAABBAOQ6aICgMB9GQggBoDASCAcBgyRVQYZkW67DoIwIiBAFogMgI4QxDAlJCYGAigJ2N+490JxvwWCAIIlyFTRIWBAHoy5jfenWDMb4EggHAZMkVUGAigJ2N+490JxvwWCAIIlyFTRIWBAHoy5jfenWDMb4HwfQDhMmSKqDAQQE/G/Ma7E4yoFkincDfQcBkyRVQYCKAnI6rxjgVSL9IlCCBchkwRFQYC6MmIarxjgdSLdAkCCJchU0SFgQB6MqIa72JaIKvVuT6ix0ngcBkyRVQYCKAnQ2W8k2FoLJDVNp5FugQBhMuQKaLCQAA9GRrjHQI4zGplYAAEED9DpogKAwH0ZEiMdwjgMKuVhQHsBfDrXzf5t0+DG7krdJ1QZZxRYcgUUWEggJ4MhfEOARxmtTIxgLUAfnpTngF+9mFoJWeFrhOqjDMqDJkiKgwE0JMhMN6lCOAwmgK4q1wE9G5oJ1eFrhOqjDMqDJkiKgwE0JMhMN6lCOAgq3o8inRJNwGsq1eBXnw7sJOrQtcJVcYZFYZMERUGAujJmH68KyEqjOkXiKYAlptx/+lXP/zj579kR4JeDOzkqtB1QpVxRoUhU0SFgQB6MqYf70qICmP6BSIpgOyDYNtRf8kHwVQYMkVUGAigJ2P68a6EqDCmXyCqAtgd98k/GjywlKNC1wlVxhkVhkwRFQYC6MmYfrwrISoMgQWieBL48WY/6G8E8Nz2WlAEMCUkJgYC6MkQGO9SBHAYRQFkFwG9L39ccw5AhSFTRIWBAHoyBMa7FAEcRlIAm7cA5TGgjwvrI0AIYFJITAwE0JMhMN6lCKAWk/HfTADld8G8zC7/fPW7q+x/vjL+ODACmBISEwMB9GQojHfRCcBr3E5TsXsB1e4Eze2ghRgyRVQYCKAnAwHUGP4LxGDsTi0YCCB+hkwRFQYC6MlAADWGN8To1bv3uwgEMAOGTBEVBgLoyUAANYbM8XuTmekSbgcdLkOmiAoDAfRkIIAaQ+YKHgTQEpVxRoUhU0SFgQB6MhBAjYEAEEA4DJkiKgwE0JOBAGoMmds4CAvg8af/yDkACYZMERUGAujJQAA1BgJwCODzW04CyzFkiqgwEEBPBgKoMRBAuwAOrwVCABoMmSIqDATQk4EAagwE4LwXEAKQY8gUUWEggJ4MBFBjcBLYdTfQJHn6cvP/V3wlpA5DpogKAwH0ZCCAGgMBOL8QZjPsLy++PbgztFEQwJSQmBgIoCcDAdQYCMAlgGzUv0tep+n9gttBizBkiqgwEEBPBgKoMfgk8DEBrLOhny+EkWHIFFFhIICeDBUBeA2X2yISC8Ri/NcTQHYOIH/xv9HAmpPAKgyZIioMBNCToSEAgyFTZ4EozUyXdL8K6MmPGw88/d0CAagwZIqoMBBAT4aEAKReNJu8E1F5O9Ml3T8HsBn2y6tBOQegwZAposJAAD0ZCgLQOmwusEAUBZA+fp0JoPg8WPnlkGZBAFNCYmIggJ4MgfFO7MKZ6RdIKimANP31f35K089vkuSZ8fiPACaFxMRAAD0ZAuMdAmiF6AlgG9srgPIKXSdUGWdUGDJFVBgIoCdj+vFuVc/wInEskBKiKwD7IIApITExEEBPxvTjHQJohwgK4Ne/bvJv9m8AEMCkkJgYcxKAybUm0493CKAdIieAn96Ud4J79sGnVmuFrhOqjDMqDJkiKoz5CMDoavPpxzsE0A5RE0D1dqDcDE6EIVNEhTEbAVhdOi8w3nESuBUiJoB19W7QXAYqwpAposKYiwDMLp0XGO8QQCtETADL7HbQX/3wj5//8oYPgskwZIqoMGYiALsxU2C8QwCtEC0BZB8A2476S24FocKQKaLCQAB9IjLemb2dQQD1Il3SVQC74z7lnUENgwCmhMTEmIcADM+bSox3Vic0EEC9SJd0vRvobtDndtAyDJkiKgwE0Ccy453B+I8A2op0See7gb4vf1xzDkCFIVNEhYEA+kRovPMc/VME0FqkS7oJYPMWoDwG9HHBV0KqMGSKqDAQQJ/ENt4hgHqRLjklgIe3V1leZpd/vvpd9p3wF18ZfxwYAUwJOeeYOXaPeQggspPACKANoiOA66QRTgJrMGSKIIAUAXhBVBhRLZBOQQDhMmSKIIAUAXhBVBhRLZBOQQDhMmSKIICUTwJ7QVQYUS2QTuF20OEyZIoggDTMewF5X32jNN4hgHqRLkEA4TJkiiCANMS7gdpIRGa8QwD1Il2CAMJlyBRBAGmA3wegchgJAbRAFAXw81//+tcfBhc6UqHrhErjnQJDpggCSMP7RjCZE8kIoAUiJ4DH7xfFvaD/s0+r9gpdJ1Qa7xQYMkUQQBqcAHQ+TIYAWiBqAni82V0DZHwnIAQwLQQBtDAQQPceCjODAFqLdElHASwrV4Ea3woIAUwKQQAtjPgFsKpncA+BmbFjIAD3N4JdfPXDP/7+L9mBoPet0wwOApgSggBaGAigew+BmbFjIADnN4KVR36yY0HcDVSDIVMEAaQIYHgQQBOiJYD9zUDT9H7B9wGIMGSKIIAUAQwPAmhCtARQ/RYwvhFMhiFTBAGkoQmAk8CtDASAAMJhnHVDOcVAAMb8UxAEMAIDAbgOASWvy5/vzC8ERQAeEARgDUEA/XoozAwCaC3SJZ2/EjL5bTbsP/452bvAKAjAA4IArCEzEQCfBG5jIIB2ARQ3hX6afzEYXwmpwkAAY0DmIoDUYPzXmRkE0FKkSzp+EGxd+SCY8ccAEIAPBAFYQ2YjAO4G2mQgANe9gD4uyuH/4p1PrdYKXSdUGXhVGAhgDMh8BFC8CfBlCM2MPwMBOO8G+vh9dvzn6ivrOwEhAC8IArCGzEkAUY13LJB6kS7h+wDCZSCAMSAI4MwMBNACERPA30Z44b+v0HVClYFXhYEAxoAggDMzEEALREsAjzf2N4GuVOg6ocrAq8JAAGNAEMCZGQigBaIlAPtP/x5U6DqhysCrwkAALZCzDQMIwIhhNDPeJ7RjWyDd0vtmcPZBAB4QBFCDIIAejHjGO4NLWuNaILbnANZJ8uyDX6EjFbpOqDLwqjAQQAsEAfRgRDPeWXyoLaoFYvwO4O9fF58ELvKKm8FJMBBACyQEAZgcrUAA+5jc1iKmBWJ/DqAa7gaqwUAALRB9ARgdrUAAu9jc2C6iBZIigDkwEEAL5GwfkB0qAKujFQhgFwTQXqRLEEC4DATQAvFfIB0HkoECMDtagQC2WdUzvId3JBbItkiX8EngcBnRCUBiz+k6kAwTgN2LVQSwDQJwFOkSBBAuAwG0QAyvJzmOQgBGDATQAkEALVEZeFUYCKAFYnk9yVHWIAEYjlUqAvA6lLWHeDweATiKdEl3ATz+/a9//eu/jXBHCATgAUEANQgC6MHwX2ve72VSrZPAvhHZjO0F8PlNeQbY/gNhCMADggBqENOh5BgMAaQqlzQhgPYiXdJRAB/31wBd8I1gIgwE0AJBAD0YMh/AQgA1iJgA7heVq0CtbwuEADwgCKAG0RZAXCeBZWbGTETe0diMU2sBLLMbQXz1wz9+/kt2JOiFX7VGha4Tqgy8KgwE0AJBAD0YsQjA7FCUdzQ249RYANkHwV6Up3+XfBBMhYEAWiDaJ4GFxkz/Jd7DlqeK2H16w4OhsitICmB33Od+gQBEGAigBSIuAJXD5mlkArD6PgDfiGzGxgJ4vNkP+hsZGH87GALwgCCAGkRdACIXzqTRCSCeXUFPANlxn+21P+skeT24VXuFrhOqDLwqjKi2epU9p/OIxt1AEUA7I6iZ6XwV0MU3+U8fF9bngBGADySarV5mz+k6oPF9AGInNKLZFfQE8Pj37xdJcnH1u6v998LYfSsMAvCARLPV6+w5HQc0vhEMAbQzgpqZIbeDNr4nNALwgESz1SvtOZ1GMwSAANoZQc0MAgiXEdVWL7XndIEgALFLmqLZFRCAIyoDrwojqq1eas9BAB2jdElTNLuCngDGDQLwgESz1UvtOQigK0PokqZodgUE4IjKwKvCiGqrl9pzEECfHiKXNEWzKyAAR1QGXhVGVFu91J6DAM7IQAAtEATQEpWBV4UR1VYvtecggDMyEEALBAG0RGXgVWFEtdVL7TkI4IwMBNACQQAtURl4VRhRbfVSew4COCMDAbRAEEBLVAZeFUZUW73UnoMAzshAAC0QBNASlYFXhRHVVi+15yCAMzIQQAsEAbREZeBVYUS11UvtOQjgjAwE0AJBAC1RGXhVGFFt9VJ7DgI4IwMBtEAQQEtUBl4VRlRbvdSegwDOyEAAtdzeIoDWqAy8KoyotnqloQQBnJOBAGqREsDD26tG7L4KoKjQdUKVgVeFEdVWrzSUIIBzMhBALVoCGPVGoEWFrhOqDLwqjKi2eqWhBAGck4EAakEAjqgMvCqMqLZ6paEEAZyTgQBqERfAxW/+8MmzXK1C1wlVBl4VRlRbvdJQggDOyUAAtUgJoMxdkvz2l83/Pn6XJM9tx38E4AOJZqtXGkoQwDkZCKAWCwFkjE7pJoB1krwuf7zb/2gUBOABiWarVxpKEMA5GQigFj0BPN7sj/s/XDfeAnx+myTJqw+tD9yfL3BOhQA8INFs9UpDCQI4JwMB1KIngM2gXxVA7STwujw38L75wLv9tO6pEIAHJJqtXmkoQQDnZCCAWiQFkGwH7nX9KqD7RfL8Q/r5Jrn4tv64u/20R6aarQDY6o0ZCKAnI6q1Fs2u0HnwtmB0PQSUXLzLf/y4qJ8FXhb/3kzzovao7yqXjDqnQgBekGi2eqkFggDOyEAAtegJIHspv8nV1VX+vwcngTfvDoo3B+vaO4Of3mwesf2dc6o0UAHEtOd4J7oFggDOyEAAtQgKIHsLsMvhG4B1Y4xPt/+8eLf7o2uqvELHqgigBRLNVi+1QBDAGRkIoBZBAVQN8OXhNUB3WyFsJqm+NXi43ky4G/ddU+UVOlZFAC2QaLZ6qQWCAM7IQAC1KAogTX/++uVm9H/6Rf06zmVlaD84vJ99bmwnAOdUKQLwgkSz1UstEARwRgYCqEVTAK4sdwP6snl+tyIA91QIwAMSzVYvtUAQwBkZCKCWuQjgT9v887xZrUQgKgyTqMzM2dZuQwDG/HMwZIqoMEziW+T21r9DZ0ZnATz+5e0iG84f/ruVAHZ7jusp6zEyNC+d6kV8E90C4R3AGRm8A6hF8h3A3xbljaDXtZO4CGAyRlRbvdQCQQBnZCCAWhQFcLf7JoBl7bO8x07vap4EZs+pF/FN3sOzidQCQQBnZCCAWgQFkN/I52UmgOx60IMR/NgFnpqXgbLn1Iv45p+rbfx6yCwQBHBGBgKoRVAAyyR5/qG4Ddxd7ZNgxz7ilZp/EExmQ4lpz/HOauVvAKkFggDOyJDar31jwdATwGN+D7dCAPW7gR67yUPLuO95KwiZDSWmPcc3q5WBAaQWCAI4I0Nqv/ZNnAKoDv2N20Efuc1bZbg3uhmczIYS057jmdXKwgBSCwQBnJEhtV/7JloB7N4B3C+63w66IgCj20HLbCgx7TmeQQDFv435pyBsxiMwvBOnALIzv88/5QJonARO048HX/WyTioH+SsHfA6nOqjQsarUhhLTnuOXVT3De8gsEARwRobUfu2bOAVQXAV0tUgu/n32Q30MP/iyR5cAbL4SUmZDiWnP8QsCKP9tzD8FYTMegeGdSAWQXQbkuB20fxCAB2T6rR4BlP825p+CsBmPwPBOrALIv91rnPEfAfhApt/qEUD5b2P+KQib8QgM78QqgDT9+W0+/L/6xqOVo0LXCZU2lJj2HM9wErj4tzH/FITNeASGd+IVQJZ/DGxzokLXCZU2lJj2HM8ggOLfxvxTEDbjERjeiVMAj//193/YHvi5f/vyy+G9Wit0nVBpQ4lpz/EMAij+bcw/BWEzHoHhnTgFUP3w1+Zn47MACMADorDVW4z/WgsEAZyRIbVf+yZ+ATQ+COYdBDAc4jPk7ov4xmD8lxpKEMA5GVL7tW/iE0D1AtBkd1NoyyCAofEfdo22eoMiSkMJAjgnQ2q/9k18ArhfNATAISCNPcfihbfVVu/9VkRpKJmFAPIhQmEzttsENXp4R0sALW8Bmndz8AoCGBaTQ+9mW73AAkEAfYIARurhHTEB3L+9erkZ9Z9eFXn1h5a7OXgFAQyKzcU3CGBoEQSQIoBRGGICSNPmLaBNgwAGBQG0QhBAxyCAkXp4R08A1c8B2AcBDMmqHo8ivpFYIAXE5rIoBNC9h8zAq9LDJ5eXqaIAxg0CGBIE0BKry6IQQPceEgNvNjMKPeIVwMer4otc7hdfcjO4VGDPQQDNmF0WhQC695AYeBFALdYCuEuS19sfmt/86BcEMCQIoBG7y6IQQPceEgMvAqjFWADZhwHycT/7QjAuA00V9hxOAtdiuEAQQPceEgMvAqjFWADL7ce/8u8FMH4LgAAGBQHUMjMB+AYBjNAjUgFkr/tflz+v+SRwdOOdb4wWiN+xe8tjYgigRw+VmVHoEakAancD5V5AEnuO3SFv35gsEN9ZQQD9ggBG6IEABgQBDIzF+C+zQLxnBgH0CwIYoUekAsgOAW3P/HIISEYAVpe9KywQ/6EbAfQLAhihR6QCyE4CXxSfA/hpwUlgGQEYffBVYIFYjN024z8C6NtDZWYUesQqgHV2E9CLq6sr7gaaSglAhoEAEIAPxDMIoBbrD4JV7wnNB8FSnT1HhSHwqTYE0CsIYIQe0Qogv/6/iPFXwiMAL4gKQ0AAhpdFIYAePVRmRqFHtAJI089f598K8IX1twEgAC+ICkNBAGaXRSGAPj1UZkahR8QCGC8IwAOiwpAQgNVlUQigTw+VmVHogQAGBAF4QFQYAieBU7PLouYjAN8ozYxCDwQwIAjAA6LCUBHA+WYGAaRaM6PQIz4BPLy9evXj5j/VvOKTwAigxkAACMAH4hkEUIudALI7P2z+Uw23gkAAdYYvxOwKHgTQMQhghB4IYEAQgAdEheENsbqCBwF0DAIYoQcCGBAE4AFRYfgvEKMreBBAxyCAEXrEJ4AzBAF4QFQYFgvE5AoeBNAxCGCEHghgQBCAB0SFEdUCQQBdozQzCj0QwIAgAA+ICiOqBYIAukZpZhR6IIABQQAeEBVGVAsEAXSN0swo9IhPALWPAPA5gDS+8Y4FMqQIAki1ZkahR4QCOLwAiKuA0vjGOxbIkCIIINWaGYUeCGBAEIAHRIUR1QJBAF2jNDMKPeYggIvf/IHvBI5qvGOBDCmCAFKtmVHoEZ8AytwlyW9/SYsvhjH+TngE4ANRYUS1QBBA1yjNjEIPCwHc3hqsGvvvBH5d/ni3/9EoCMADosKIaoEggK6JbmYmZlxe3m7jB7IVwOPN/rj/w7X1WwAE4AFRYUS1QDoFAaQRzszEjP347zdH3R/fSQD5DYFafrYJAvCAqDCiWiCdggDSCGdmYsbtrYUB+hC6CiB5X/685iqg2MY7Fki9SJcggDTCmZmWcXtrYYBehK6HgJKLd/mPHxfmZ4ERgAdEhRHVAukUBJBGODPTMkwE0A/R7STwXX4B6NXVVf6/nASOa7xjgdSLdImPALyjNGYiACvGbT0GkFOTdxNA9hZgFy4DTeMa71gg9SJdggBSBGDL0BVA1QBfGo//sxWAzO3vVRaIzsx0CQJI4xLA9BKxEEBfRue7gf789cvN6P/0iw8DSp2o0HXCqMY7oS/A0lggBgwE0CPTj3eGEAQwlMHtoAcxvCFKX4ErsUAQQJ+IjJlRzYzAArE/AoQAxmL4UVYrCwMILRAEgAAmhCAAB8JOAI9/ebvIPgHw8N+H1DpaoeuEMuOd99C9WpkYQGaBpAgAAUwJQQBDGV0F8LdFeSPotfVVoOEJwH/oRgCjMBBAjwiMd3aQSARg8UngcQRwt/smgGVy8e2wZs4KXScUGe/8x+5VPQObiCwQBJD/u9vDTIYZlTETAdTjOS8G9wIaRQDrbPR/mQkgux70xcBqrgpdJ9QY7wzGbgQwDgMB9IjCeGcGiUYABncD7fcuopsAlkny/ENxG7i70G8FYXr+FgGUDASAACaExCMAg+8D6PUuouu9gC6+Le8DGvjdQK3P3w4CIYBxGAigRxTGOzNITALwL9LnXUSP20HHIAD/Qddk8LYZ/xFACwQBdIzCeJdHZfBWWCBWXwnZ/V1EVwHs3gHcLwIWgMGwiwBaGQgAAQyMyuCtwBD9TuDszO/zT7kApj0J7DNapjbjLgJoZSCAcwpAYazKgwBsGaICKK4CulokF/8+++F96zSD01kA3gOmybhrDxmGSBFAGwQBnJGBAIwZqgLILgOa/HbQGkdvjF69W4z/CKAFggDOyEAAxgxZATx+N9r433XPMR+6JxUAdwMdgYEAzsxAAMYMWQGk6c9v8+H/1TcerRwVOk1lMO7aCMDm8M0//W8ohwDaIAjgjAwEYMwQFkCWfwxsc6JCp6l0BGBy+EZpvEMA9SJdggBSBGDNsBBAP0Y3AfztK+vjPtUKXSYyGbxtxn+TwzdK4x0CqBfpEgSQIgBrhqgAHm/sj/xXKnSZSEoABodvlMY7BFAv0iUIIEUA1gxRAdh/+vegQpeJxAQQ1XiHACoZvOcggIFRmRkFhqgAinsBjZXzCcDu8vtoxjsEUMtcBOB9y7E8CMCWISqA7INgz+y/DX5bodNUNq/eLcb/qMY7BFDLPATgf9PhIgjAliEqgMe/f51dAvr0qsirKe4FZHT4xmD8j2q8QwC1zEIA/l87UgYB2DJEBfBwnVQzyc3gzI7fe5+/jWq8QwC1zEEABl88WEZEACbHsxBAFlkBaN0+J5rxjgVSywwE0PNLA49FQgBG72YQQBZdAUjdPiea8Y4FUgsCOFsPI4jVuxkEkOXoJ4HHyfnuBpoy3o3FiGaBxC+A23om6mEEMXs3gwCyKAvA6Pg9490IjGgWCAI4Vw8biNbbGQQwIGf+UnjGuzEY0SwQBHCuHjYQBODH6CCAx5/eXl39xv4uoLsKXSdkvGtCVBjRLBAEcK4eJhCxmYlRAJ/fjPQ9ALsKXSdkvGtCDBhcF1tN/AIQe9GMACoRFMD+CqCxDIAAPCDeDD4ZdxgEcLYeFhAE4Mk4KYC7/eWfxt8FvKvQdUIE0IT4Mrg3Ri0I4Gw9LCAIwJNxSgCPN9mF/z/8n+x9wAuvXu4KXSdEAE2IJ4O749UzAwFoXTkZlc0iFcD7/HZwYx0DQgAeED+G2f01Ylkg8xCA1GenEEAlegLITgFkt4K+X5h/AnhXoeuECKAJkRGAbzQWyEwEoHT3BARQiaYAsoF/+78jBAF4QLwYq3qG9/COxAJJ5yKAYuBU6BHX8SwEMCAIwAOCAGoQBBAYI6rjWQhgQBCAByQWAeQb7OQLJEUAZ2dEdTwLAQwIAvCAIIBaEQQQGCOq41neY3c2essJIPsqsJeVrwSb5BvBUgTQBonlJDAC6BOBscqMIVNkcsblNnICqGWi7wNAAC0QBFArggACY8gUmZpxuY9XBwRwckDr/wAAIABJREFUBgYCqPXwDQLok6nHKkuGTJGJGZeXRgZAAGdgRCIAw08CewYB9EkE450tJHzG5aWVAfg+gDMwYhGA3b2APIMA+iT88c4YEj4DAZwOAmhCZO4G6hkE0Cfhj3fGkOAZl/UM74AAzsCIRwBW3wfgGQTQJ8GPd9aQ4BkIoEMQQBOiwvANAuiT4Mc7a0jwDATQIUrjnYoAvIMAWop0CQJgZuwYCKBDEEALxDcIoKVIlyAAZsaQwUng00EALRDfxCYAkyJdMkAABp/zLxL+eGcMCZ+BAE4HAbRAfIMAWop0CQJgZgwZCOB0EEALxDcIoKVIlyAAZsaSYTX+I4BzMBCAcQ8E0CcRjHe2kBgYRuM/AjgHAwEY9zARwFl3YQTAzNgyduO/XxEEcAYGAjDuEd5QggDimhmF7xQoXv17Q/oUQQCDGAjAuEd4QwkCiGlmRL5VTO8bwc4QBOAD8Q0CGMhAABHNjMr3CiOAYzH6mA8CqDEUeoQ3lCCAeGbm9tbIAAhgQOYoAJmLXhDAUMYwAZgcaVYYM80YAkVub60MgAAGBAH4FPENAhjIGCIAoyPNAmOmHUOgCALYBwF06oEAKpl+DzaEjCkAqyPNMks8vLXW/mAzAyCAAUEAPkV8gwAGMvoLwOxIs8wSV1hr/mMmAqgEAXTqgQAqiWUo6cfoLQC7Aw0yS9yG4UdBAF4MBNA/COAwKkMJAgiP4b84lATg/dYOARwLAmgp4hsEMJDRVwCmrzMN5lRirRksDoMx02jFGKxZBHAsCKCliG8QwEDGZAIweQ+hsdYsloeMACzcjgCOBQG0FPENAhjImEoAJu8hUom1ZrJAVARgIncEcCwIoKWIbxDAQMZEAjAZZwqQz6NNGCoCsFioNnZHAMeCAFqK+AYB1BBdIdOcBLYZZwqSx4NNGDZKtBgzDd5WIYDBQQA+RXyDAA4A3fdfBBCRALxPrNjMCwI4GgTQUsQ3CKD6+B777yQCMBpnCtTwx9owlATge2kVAhgeBOBTxDcIoPLwPjvwJJ8ERgCNWAnAj4EAhmduAtjdFBgB2DLOey3IJPcCikoAOieByzZeD5aZGQRwJAighYEAykePLQD/u4EigEY0BMBJ4OFBAD5FfIMAdg/utw9P830AZuO/wlpDAPWYCaDz2IAA+gcBHGb6vc8Cch4BSIyZJcmryLaMN0BhzDRYMwhgcBCATxHfIIDdgxHAsCrDEZcGsyIiABOZIYCjQQAtRXyDAHYPDkEANuNMATIrMphxafAdaSoCMFog2X/99msEcCQIoIWBAMpH9xpcJxKAxYvmPCoveH1nREYAJm+JEMCRIICWIr5BAPtHByEAqy8WjuqQt4QADE6KIIBjQQAtRXwjIACjvW8uAjAYZ7IggBpE4XUMAjgWBNBSxDcIoPLwPkPahAKYfqy6rWcYBgG09EAArsQmAO8gANsivQY0BIAARmEgAFcQQEsRAwYC2AO6j2cIAAGMwkAAriCAliIGDARQRXSFIAAEMAoDAbiCAFqKGDAQwBDGrAXASeDxGJ779eVlDolQABaDt5kAPCI13okIwOS6FgTQJwigBlFheA0wl9sggNEYCgIwGwYkBOA7jlgV6cVAAAhgFIbPAHO5T8dHIID+QQCHOevFN2MW6cWYtwB6XjPrCAJoYXgMMJeXvQ2AAPoHARxGYSixKNKPMXMBmGgbAbQwhg8wl5f9DYAA+gcBHEbhYIJBkZ6MuQug1zWzjiCAFgYCcMRKAN5BAIdBAPm/Tz9Eas2f9ZpZRxBAC2PwAHNZT5cHIYD+QQCHUbii3L9IXwYC0JgZBLB/cPwC8I0CQ2oYQABDGQhAY2YQwP7BCCAEhtQwgACGMhCAxswggP2DEUAIDKlhAAEMZSAAjZlBAJVHR38S2DcKDKlhYHIBcBK4X2QEoDIzCKDyaAQQAENlzymCAAYyEIDGzCCAyqMRQAAMlT2nCAIYyEAAGjODAKoP7zv+I4DzM1T2nCKTCyA1HP8NlshsBBDL1cwI4ODxPcd/BHB+hsqeU2R6AaQ247+JQxBAryIKM+M7Zu4gMvs1dwNtj8LgbcFQ2XOKCAjA5G6gRu8ixhRA/rpMas37FlGYGQRQR+SQcwrg89skSV59OP7ru6TMi0aFjk+jMHhbMFT2nCIKAjD4PoDzn0foK4C+782dmBQBNBgqMyMggJ4MAwGsi4H94v3RXy8VBHDeo8SOWIx3sQnA9DyC5zuJjhP2FED/o7MuTooAGgyVmZmjAO4XyfMP6eeb5OLbI79+uD78c7VCxyfy3urPfZTYEYsjHkoCMLFZ7AIYcH2GC5QigAZDZWbmKIBl8vzT5n8ebw5f29d+fb/I/9laoeMTaZxr1OghMwxYWNV/z7mtxwfVccJeAhhyhbaLlGqseQQwEiMwATxcJ8VBnnXy5Ef3r9fNQz+7Ch2fKbijxCP2kBkGrGymIIB+CxUBIIBRGIEJYDfu74b81l/fJe9bHlxU6PhMKp83UuihMgyY2SxuAVzWM6hfiUol1jwCGIsRmADukvLYzuNN8tr96+WTf/16kSSvfmip0PGZFAZelR4iw4DdzCCAjoluzIxqZmYogGVlpH/h/PXmfUCZ1w3CWQRgM0TI9EAAIxRBAH0hCGAERnACeNH4qfnr+0Vy8dWn9NfvWgyAAPpHQwCGM4MAOia6MTOqmUEAjl+vk/IcwHJ/qvhP2/yzW25vO07Y/uBafFDT97i89C1iEcOZ8ZyXCRZqQwDH0QcZ1m9AySMM3xistfhmxmSBeKYf40wC2OXhuvEWgHcA/cM7gGNVBjJmcxWQ1IvmqGaGdwAnft34uEDKSeAhQQBHugxFIIC+EAEBlItTZGZmKYAuJ4Grf0AAsQhA5yRwtctgwogC0PokcDwC2B9U8y+CALKMdRnoLggAAdRiI4Cijc/jxxRAajT+I4DDx9ssVASwy1gfBNum7oOUTwIPCQJor+P18FEFkBoOVQhg+3BLrSKALCPdCmJ3ROh+0fhIMPcC6h8RAah8EnjXxuvh4wqgPFw99YvmHSN8AdidWEEAu4x0M7h1eVPQzT8bN4U7kwAuTb53xL+H3t1AfWJlszkIQGDMrDAEBFAM2wjgkBGaADrdDnoz8F+8S9PPb5LmPYHOJgD/o8RWK9mkh4QADM69IoA+iUcAu5F78MVjttdWIYAsA74Q5uPBN7/sPvJ1+Ots6M//+a5ZoePzKIyZUhuKhABErFo28e6BAPpATC7gGTpyIwALhvlXQu4EUPtKyMfvX26G/y8aXxyJAIYyVAQgsUAsiiCAvhCbC3gGDt0IwIIRxpfCm231KitZYRgoggAOeiCAPhCjC3iGjd0IwIKBAHpDVBgIwLgIAugLmVQAnAS2YCCA3hAVBgIwLoIA+kKsLuAxeQswsEqJ0tiMEUB7EEArw2LURAAHPRBAH0hUApj6rPiuBwJoxGK8UxFAvq369zD6UAMCOOyhLoBipEMATcrAJltSigDyqApA5MOzdhc/e/aw+lgzAjjsoS2A3daDAOqYgUV2oBQB5BEVgMrtcya/+LmMyS0Y8iCAgx7SAjB9vRv+SeAqxnfMzCoggFRVAFa3HJt6rLJ6z3p7a2YAka1+NgLwWOBmRzziEcB27PaaGcN3EQhgQE7vOSbjnYAAzLZ6BNCauAVgtvVEJQD/mbF8X4UABiQwAXi9CbHZ6m/rGUxCALUeCKAPRODNsPfMmBZBAANycs+xGe9sBODT4bKeoS0QQHuiFoDZ1qMhAJUTGrZaRQADEpIAvEooCkBh8EYAXRKbAOxO4Gb/FRGAxTUVCKAeHQH4tUAA4zHyIICuqNSjSAVi8qGGs453bRUslyoC6J9wBOBZw25jszsCpDB4Swlg8J6DAKYpggDaekQlAJmTwL49zLa1qASQLwoE0OHRpgcrEEDxaKvFWjwYAfRPMALwfieCANoA/gPaNgigKyn1KbKHIIAGZ+DjD4IAakEAdZDV+D+5AGxGtDIIoCsp9Smyh4QvAKOlaroZI4BGLMY7AQEYXXOcPdho/J9aAFZDWpG4BaBz6fwOggBaIEOb7IMA6tG4F5C/AExeKRQPtxn/Y/lodJHIBaBy6fweggDMGNUggHpE7gZqcCjKavwvFTCYsg0CqLbJ/issAJFL53dVYhCAxat3BOCVcL4PwEAA3nuO1pjpJ4DLevy6xC8AiUvntzUM1piCAPzfV1lvxgigEYsPcakIIKoXzQhg/++OT2Bw5CX8MdOqiAnDd14QgF/CEYDJyeioxsyoZgYBdH680TpTmJmC4XMNPwLwS0AC8LsXUKVILGNmVDODALo+3GqlCcxMBYIAUgRwCmJw8U1MY2ZUx7MQQNeHI4Dao003YwTQiI4AvL4PYF8EAZSPRgA9M/2YabgJTj8zVQgCSBFABwhj5kGdeGYGAXR8NAKoPxoBeAQB9Hu00pjpu0BsdxwEcA4GAmh5uOVmjAAaQQCVR8ckALMLCvMggI4IlXOesQjAdjNGAI0ggOrDhV40+y8Qyx0HAXQClBE44hGNAEw3YwTQCAI4eLzOi2aLBVLMiOcenAcBdHm899aDANoRVpsxAmgEARwCyhh8icrkM5PajIt5EECHh/sP3QhgNEYRBFAPAqgjLIpkEZgZBNAnpidwLSgDGSUo+y8CqAUB1IMAWhgIoB4EcPrRJoO31VFIBNAaBFAPAmhhIIB6EMDJB1sbYChgi8n+iwBqQQD1IIAWBgKoBwGcfLDZ8XuTc54IoDUIoB4E0MJAAPUggJMPNj6BiwBsGUUQQD0IoIWBAOpBACcfjADcEAFGEQRQDwJoYSCAevyKlANizAIwvIJTYGbsGAhgFwRwEqLCQACNeEB2gyIC6FEEAdgyiiCAehBAC8NCANPe27rCmHoo6T8qIgCPHlUGAqgHAdRjMN4V27vM4G2xsXkLYOpvt6kwwvuAbJACGDKjx4ogAFtGEQRQj/d4t9vmDT6BKzPe+b54n/z7LSuM8F4YhymAAW91jhVBALaMIgigFu9X71bXPWjtOZ4Dr9k33CvsOQigO6DnyY5jRaaeGSOGzGZcBAEcxP/Vu837Xrn3zn4CuL21MIDKnnP+qyNDFUDvy52OFZl+ZhBAa4+IBOA/7g55fXeCMpBRglIEMAoDAZyPwcyMwSiCACoxGHdNBGBjkYKUCgjgth6PIgJ7DgI4M4OZGYNRBAHsYzDuDtq9xyiyI6UIYBQGAjgfg5kZg1EEAeyjIgAbi5SoFAGMwlA/CZyHMdO2iNbMCAigH0NcABbjLgJoCwI4eDQCGARhZmwZRRDALgjgCIOTwDUGAjgfQ2Ct2TFkNuMiCGCXMQbvQSURgKNH1kRgz/EcBgasWgRgxIhqZhBA/wQhgOhOAlsIwGJppBpDSf9ViwCMGFHNDALon7FPAiOA9tiN/54GkBhKdrMydM9BANMU0ZoZBNA/owtgyBv8cYqUpFRCAL73ArJaHipDSTkbCKAHhJmxZRRBAPvYjLsWI5XeeOd9J2e78V/DiJNeQI0ApimiNTMIoH9G/yRwlWJxkEDliIfJrfw9rv9BAAf/7vi44Gb0GISZsWUUQQDVmL3u9l5BNuO/lACGMy7r8SwS3FCCAIwYUc0MAuif8e8GWmKy//pubJcRfR8AAvBhIAAjRlQzgwD6p+v3AahsKDICsFggCGAoAwEYMaKaGQTQP13voqWyoSCA4tEI4PDfHR8X3IwegwjMTBGVmUEA/YMABjImFQAngRGAxMwUUZkZBNA/CGAgAwHUGAjgfAyZtVZEZWYQQP+EJgAZBgKoMRDA+RiBjneOqOzXRRBAPVFt9VLjncfbGavxX2uBIIA+kODGO0dU9usiCKCeqLZ6qfHO53iW0fivtUAQQB9IcOOdIyr7dREEUE9UW73UeOd1QsNm/NdaIAigDyS48c4Rlf26CAKoJ6qtXmq88/4wWUaJaYGoC8ACIrMrFEEAtSCAehBAG0NAAPEtEATQByIwMyYMlc24CAKoBwG0MRBAjYEAukVmVyiCAGpBAPUggDYGAqgxEEC3yOwKRRBALQigHgTQxkAANQYC6BaZXaEIAqgFAdSDANoYCKDGQADdIrMrFEEAtSCAehBAGwMB1BgIoFtkdoUiCKAWBFBPVAJIrXoggBoDAXSL1q4gsz8JMIoggHoQQFsPP0hxCT8CGMpAAP6MIgL7kwUjzAWCAPpDVDY2H4jR16whgP2/Oz5OYIjQ2hUU9icLRpgLBAH0h6hsbB4Qq9v4IID9vzs+TmCI0NoVFPYnC0aYCwQB9IeobGzDIWY38lQRQDkfCKBTtHYFhf3JghHmAkEA/SEqG9tgiN2t/DUEsJsTBNApJruCQQ9DiAADAQwMAhjYAwFsH2/4bgYBnK+HIUSAgQAGBgEM7DEUclmPb5FpF6qtzBDA+XoYQgQYCGBgEMDAHgigeLTpvCCA8/UwhAgwEMDAIIAzQxDAkR4I4Hw9DCECDAQwMAjgzJCoBGA4MwjgzD0MITExEEAjCMAUYjf+T79AEED/IABpBgJoBAGYQhCAA9WnSMAC0GLIFFFhIIBGEIApBAE4UH2KIAAjhkwRFQYCaAQB2ELMxn+BBWJ8PgMBnJshU0SFgQAaQQDGEKvxX2CBIIDAGTJFVBgIoBEEYA3ZDZjBLxAEEDhDpogKAwE0ggDsIeVoGf4CsT2fgQDOzZAposJAAI0ggBEg0SwQ0/MZCODcDJkiKgwE0AgCGAESzwIxOp6FACZhyBRRYSCARhDACJCYFojJ8SwEMAlDpogKAwE0ggBGgES1QM6/hSAAI4ZMERUGAmgEAYwAiWqBIIBgGTJFVBgIoBEEMAIkqgWCAIJlyBRRYSCARhDACJCoFggCCJYhU0SFgQAaQQAjQKJaIAggWIZMERUGAmgEAYwAiWqBIIBgGTJFVBgIwBGBFaQy3llAEIAXAwEYMWSKqDAQgCMCK0hlvLOAIAAvBgIwYsgUUWEgAEdUVpAKAwHUeiCAEBkyRVQYCMARlRWkwkAAtR4IIESGTBEVBgJwRGUFqTAQQK0HAgiRIVNEhYEAHFFZQSoMBFDrgQBCZMgUUWEgAEdUVpAKAwHUeky65yCAKSExMRCAIyorSIWBAGo9EECIDJkiKgwE4IjKClJhIIBaDwQQIkOmiAoDATiisoJUGAig1gMBhMiQKaLCQACOqKwgFYaCAAx6mDAQQLAMmSIqDATgiMoKUmEggH0QQLAMmSIqDATgiMoKUmEggH0QQLAMmSIqDATgiMoKUmEggH0QQLAMmSIqDATgiMoKUmEggH0QQLAMmSIqDATgiMoKUmEggH0QQLAMmSIqDATgiMoKUmEggH0QQLAMmSIqDATgiMoKUmEggH0QQLAMmSIqDATgiMoKUmEggH0QQLAMmSIqDATgiMoKUmEggH0QQLAMmSIqDATgiMoKUmEggH0QQLAMmSIqDATgiMoKUmEggH0QQLAMmSIqDATgiMoKUmHIFBFgIIBgGTJFVBgIwBGVFaTCkCkiwEAAwTJkiqgwEIAjKitIhSFTRICBAIJlyBRRYSAAR1RWkApDpogAAwEEy5AposJAAI6orCAVhkwRAQYCCJYhU0SFgQAcUVlBKgyZIgIMBBAsQ6aICgMBOKKyglQYMkUEGAggWIZMERUGAnBEZQWpMGSKCDAQQLAMmSIqDATgiMoKUmHIFBFgIIBgGTJFVBgIwBGVFaTCkCmiwkAAITJkiqgwEIAjKitIhSFTRIWBAEJkyBRRYSAAR1RWkApDpogKAwGEyJAposJAAI6orCAVhkwRFQYCCJEhU0SFgQAcUVlBKgyZIioMBBAiQ6aICgMBOKKyglQYMkVUGAggRIZMERUGAnBEZQWpMGSKqDAQQIgMmSIqDATgiMoKUmHIFFFhIIAQGTJFVBgIwBGVFaTCkCmiwkAAITJkiqgwEIAjKitIhSFTRIWBAEJkyBRRYSAAR1RWkApDpogKAwGEyJAposJAAI6orCAVhkwRFQYCCJEhU0SFgQAcUVlBKgyZIioMBBAiQ6aICgMBOKKyglQYMkVUGAggRIZMERUGAnBEZQWpMGSKqDAQQIgMmSIqDATgiMoKUmHIFFFhIIAQGTJFVBgIwBGVFaTCkCmiwkAAITJkiqgwEIAjKitIhSFTRIWBAEJkyBRRYSAAR1RWkApDpogKAwGEyJAposJAAI6orCAVhkwRFQYCCJEhU0SFgQAcUVlBKgyZIioMBBAiQ6aICgMBOKKyglQYMkVUGAggRIZMERVGcAL4/DZJklcfTvzaMRUCmBYSEwMBhMiQKaLCCE0A6yTPxfujv3ZMlSKAaSExMRBAiAyZIiqMwARwv0ief0g/3yQX3x75tWOqvELXZ1JZQSoMmSIqDAQQIkOmiAojMAEsk+efNv/zeJO8OPJrx1R5ha7PpLKCVBgyRVQYCCBEhkwRFUZYAni4ToqjOuvkyY/OXzumKip0fSqVFaTCkCmiwkAAITJkiqgwwhLAbkTfjfFtv3ZMVVTo+lQqK0iFIVNEhYEAQmTIFFFhhCWAu+LYTn5057Xz146pigpdn0plBakwZIqoMBBAiAyZIiqMsASwrAztL5y/dkxVVOj6VCorSIUhU0SFgQBCZMgUUWGEJoAXjZ+av3ZMVVTo+lQqK0iFIVNEhYEAQmTIFFFhzEUAf9rmn4QEl4YApi5ESP/wDiBchkwRFQbvAEJkyBRRYczlHcCuQtenUllBKgyZIioMBBAiQ6aICiM0AXifBCYkxLAZkwjiKQCDy0AJCTFsxiSCeArA/4NgndP5WNFMGDJFVBg6Rcbmq8woMzMCY9IiE9wKonNUVpAKQ6aICkOnyNh8lRllZkZghCUA/5vBdY7KClJhyBRRYegUGZuvMqPMzAiMwATgfTvozlFZQSoMmSIqDJ0iY/NVZpSZGYERmADSjwdf9bJOyoM9h7+u/3NIVFaQCkOmiApDp8jYfJUZZWZGYIQmgMMve9wJoPNXQnaOygpSYcgUUWHoFBmbrzKjzMwIjOAEQAghJIIgAEIImWkQACGEzDQIgBBCZhoEQAghMw0CIISQmQYBEELITIMACCFkphEWwOPN8PvIZfn1+5e+H0VL048bxsWXv3gxstwvPO6JkWb31y4z/NZKafr560WSPH33aTBgmezjUeSn/DOCPwwH5JA32ZoZPi/Vzcv/Q4udnmZQ2IzrYTOuxHczFhbAnceNRDdZL8pVXP8+gh55vClvaOG11RcgP8bSYIP9WC6R54M3N5s95zv/FZM+Ln3XTGXzWvvftqTL0wwKm3E9bMbV+G7GugLYvFjw2XXuF8mzjZw/3yTDv5Bgs6VcvNsw3njuxPnMeO05D9feO2+2fTz7kD7+2e/lV5GlxxLZ9Ni84vFbMdk+/HwzM98NXqyVzcvixoUdnmZQ2IzrYTOuxnszVhXAY+ZXn+11a8bNi5bBLxU2izRfuQ/XXprPQX57zv1i+OudMrsF4fuaNK3eAGpQjxcHfYZks0CL3f9u2DBwsHlZ3Lq8w9MMCptxLWzG1RL+m7GoALIjY1c+a3i/GNbDt9ndNtb8Zvu+ZX7vt+es/cem3XLYjgfDsxlJhtfZfVeQzx58t52ZYa8pDzYvky8vOv00g8Jm3ESwGW9jsRlrCmAzMxfvjPZH3/NWWTz3nI3fPfbfguB9fHrp8UqlgfKYmd3rUJ89Z1n5zun+S+Zw8zL5+tLTT+MZNuMibMbVCv6bsaoAvvxk9YLMc5vN4vli437x5EfPFssn/5pd+uBxycFmE/N7/7/PZnH4nZH0P+KyH8qWA7ocbl53lb3QahG1PI1n2IzzsBnvY7IZawogzS5YM9p1fE70FPlp4fWqI7e7356zkbrvJQfZ64LH7GrAp++G9yjiuUTXi+zs2a//xWeJ7PacYfvfweZVfRlmexLAcCtmM94h2Iy3sdiMRQWQxWbX2Z1nGZj8Errf+rzrzE/w+O05m5m4+GqztX03fNfZIN7d+F4/l8fr0Gle5U1e45nHpe13lfe7A8usm0fGPQ+RHH8av7AZ7xBsxtX4bsaxC2Cz4ftRHv7D1eZN6xfDN7bi4K3fnrO7XGH4q5bsCo7sJUt24YDfhn/nezDib8WF3Bcer+F2b9/vBs9NUAJgM97VYDM+CAI4mke/y3TL+Lx5Lo/IGRzCzTL8Sr5sz9lubF5VvK57y7LMX4o+erwMLCDlMDAHAbAZb8NmXAsCOJZsx7E4Z+RxwXB5bsZozxl+mPp+t/d7Xg7ue/nd2mQP3n649fXgQTsgAbAZ78Jm3OiBAJyx2nG8Nthi8zDac4aPUfuDyJ7nOn0/gLOsHPf0uQrj++xykg/Drwo5w0ngg6cZHjbjahE244NwEtidhzdJ4n2tQJHBG+zu7leeN0DxLlLZTr1e6Bq8dbbZg4sM3/3Gvwz08GkGh824EjbjWiK9DDSL767z+drs9l7DV7LxnjN8jNrPgt8W630/Ads9Z/jno8b/INghfWjYjGuPZDOuJs4PguXx3HWyz8l5vl1dGh1vTH3fO++KeBy5vLN5z+r9CXyTHrvFOfydfHOHMb8VhAWUzfgwbMZ1RIy3gsjjt+t437k2PbhszZfle/1c8WjPW4IVr1T8PgDjfRS4cunbcNIW4vFpzv3mNd7N4FJvAbAZ18JmfBjfzThaAdwZXDiX7X3vPqW/fu1/Es5vg8uL5Df09ZipzRL54hff69YMbsK4tLiQu7jDsc+FjfvNa8zbQfsKgM24Hjbjg/huxrEKYP+pc68Pnr8x+NKHPJ6vOD6XRXw+dbL7BosvPRgGN+PaXvqWeHwP0m7NPPO4n/tu8/pYLtwxvhDGTwBsxs2wGVfjuxnHKoD77Rcp+W33+W1HLr7w/7JA37ecj98bFPk5/8q4b7wYFtfK/2TQI18gT78avvNVN68RvxLSTwBsxi1hM67EdzMWFgAhhJAxgwAIIWSmQQCEEDLTIABCCJlpEABfbO7/AAAAXElEQVQhhMw0CIAQQmYaBEAIITMNAiCEkJkGARBCyEyDAAghZKZBAIQQMtMgAEIImWkQACGEzDQIgBBCZhoEQAghMw0CIISQmQYBEELITIMACCFkpkEAhBAy0/x/BhOmmWdyydkAAAAASUVORK5CYII=" class="img-fluid figure-img" width="768">
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-fig_d3-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure 9: Predicted probabilities of voting for a green party by education, ESS wave and gender (Appendix)
</figcaption>
</figure>
</div>
</div>
</div>
</section>
<section id="multilevel-model-voting-for-the-greens-education-ess-round-age-group-three-way-interaction" class="level2" data-number="3.7">
<h2 data-number="3.7" class="anchored" data-anchor-id="multilevel-model-voting-for-the-greens-education-ess-round-age-group-three-way-interaction"><span class="header-section-number">3.7</span> Multilevel model: Voting for the greens – education, ESS round &amp; age group (three-way interaction)</h2>
<div class="cell">
<details class="code-fold">
<summary>Show the code</summary>
<div class="sourceCode cell-code" id="cb13"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="co"># predicted probabilities: by age_group</span></span>
<span id="cb13-2"><a href="#cb13-2" aria-hidden="true" tabindex="-1"></a>pp_three_green_age <span class="ot">&lt;-</span> <span class="fu">predictions</span>(three_green,</span>
<span id="cb13-3"><a href="#cb13-3" aria-hidden="true" tabindex="-1"></a>  <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&quot;gndr_fct&quot;</span>, <span class="st">&quot;age_grp_fct&quot;</span>, <span class="st">&quot;edu_fct&quot;</span>),</span>
<span id="cb13-4"><a href="#cb13-4" aria-hidden="true" tabindex="-1"></a>  <span class="at">newdata =</span> <span class="fu">datagrid</span>(</span>
<span id="cb13-5"><a href="#cb13-5" aria-hidden="true" tabindex="-1"></a>    <span class="at">age_grp_fct =</span> <span class="fu">c</span>(<span class="st">&quot;22-29&quot;</span>, <span class="st">&quot;30-65&quot;</span>, <span class="st">&quot;above 65&quot;</span>),</span>
<span id="cb13-6"><a href="#cb13-6" aria-hidden="true" tabindex="-1"></a>    <span class="at">edu_fct =</span> <span class="fu">c</span>(</span>
<span id="cb13-7"><a href="#cb13-7" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;(below) lower secondary&quot;</span>,</span>
<span id="cb13-8"><a href="#cb13-8" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;upper secondary&quot;</span>,</span>
<span id="cb13-9"><a href="#cb13-9" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;tertiary&quot;</span></span>
<span id="cb13-10"><a href="#cb13-10" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb13-11"><a href="#cb13-11" aria-hidden="true" tabindex="-1"></a>    <span class="at">grid_type =</span> <span class="st">&quot;counterfactual&quot;</span></span>
<span id="cb13-12"><a href="#cb13-12" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb13-13"><a href="#cb13-13" aria-hidden="true" tabindex="-1"></a>    <span class="at">re.form =</span> <span class="cn">NA</span></span>
<span id="cb13-14"><a href="#cb13-14" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb13-15"><a href="#cb13-15" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-16"><a href="#cb13-16" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(</span>
<span id="cb13-17"><a href="#cb13-17" aria-hidden="true" tabindex="-1"></a>  <span class="at">data =</span> pp_three_green_age,</span>
<span id="cb13-18"><a href="#cb13-18" aria-hidden="true" tabindex="-1"></a>  <span class="fu">aes</span>(</span>
<span id="cb13-19"><a href="#cb13-19" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> age_grp_fct,</span>
<span id="cb13-20"><a href="#cb13-20" aria-hidden="true" tabindex="-1"></a>    <span class="at">y =</span> estimate,</span>
<span id="cb13-21"><a href="#cb13-21" aria-hidden="true" tabindex="-1"></a>    <span class="at">ymin =</span> conf.low,</span>
<span id="cb13-22"><a href="#cb13-22" aria-hidden="true" tabindex="-1"></a>    <span class="at">ymax =</span> conf.high,</span>
<span id="cb13-23"><a href="#cb13-23" aria-hidden="true" tabindex="-1"></a>    <span class="at">color =</span> edu_fct</span>
<span id="cb13-24"><a href="#cb13-24" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb13-25"><a href="#cb13-25" aria-hidden="true" tabindex="-1"></a>) <span class="sc">+</span></span>
<span id="cb13-26"><a href="#cb13-26" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_pointrange</span>(<span class="at">position =</span> <span class="fu">position_dodge</span>(<span class="at">width =</span> <span class="fu">c</span>(<span class="fl">0.2</span>))) <span class="sc">+</span></span>
<span id="cb13-27"><a href="#cb13-27" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb13-28"><a href="#cb13-28" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> <span class="cn">NULL</span>, <span class="at">y =</span> <span class="st">&quot;Predicted probability&quot;</span>,</span>
<span id="cb13-29"><a href="#cb13-29" aria-hidden="true" tabindex="-1"></a>    <span class="at">color =</span> <span class="cn">NULL</span></span>
<span id="cb13-30"><a href="#cb13-30" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb13-31"><a href="#cb13-31" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_viridis_d</span>(<span class="at">option =</span> <span class="st">&quot;plasma&quot;</span>, <span class="at">end =</span> .<span class="dv">8</span>) <span class="sc">+</span></span>
<span id="cb13-32"><a href="#cb13-32" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_light</span>() <span class="sc">+</span></span>
<span id="cb13-33"><a href="#cb13-33" aria-hidden="true" tabindex="-1"></a>  <span class="fu">my_theme</span>() <span class="sc">+</span></span>
<span id="cb13-34"><a href="#cb13-34" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;top&quot;</span>) <span class="sc">+</span></span>
<span id="cb13-35"><a href="#cb13-35" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>gndr_fct)</span>
<span id="cb13-36"><a href="#cb13-36" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb13-37"><a href="#cb13-37" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(</span>
<span id="cb13-38"><a href="#cb13-38" aria-hidden="true" tabindex="-1"></a>  <span class="at">filename =</span> <span class="fu">here</span>(<span class="st">&quot;figs&quot;</span>, <span class="st">&quot;final&quot;</span>, <span class="st">&quot;appendix_fig_d4.png&quot;</span>),</span>
<span id="cb13-39"><a href="#cb13-39" aria-hidden="true" tabindex="-1"></a>  <span class="at">units =</span> <span class="fu">c</span>(<span class="st">&quot;in&quot;</span>),</span>
<span id="cb13-40"><a href="#cb13-40" aria-hidden="true" tabindex="-1"></a>  <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">5</span>,</span>
<span id="cb13-41"><a href="#cb13-41" aria-hidden="true" tabindex="-1"></a>  <span class="at">dpi =</span> <span class="dv">300</span></span>
<span id="cb13-42"><a href="#cb13-42" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<div id="fig-fig_d4" class="quarto-float quarto-figure quarto-figure-center anchored">
<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-fig_d4-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<img role="img" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABgAAAAPACAMAAADE4EJKAAAA+VBMVEUAAAAAADoAAGYAOjoAOmYAOpAAZrYNCIc6AAA6ADo6OgA6Ojo6OmY6Zjo6ZmY6ZpA6ZrY6kLY6kNtmAABmADpmOgBmOjpmOmZmZgBmZjpmZmZmZpBmkJBmkLZmkNtmtttmtv+QOgCQZgCQZjqQZmaQkGaQkLaQkNuQtpCQttuQ27aQ29uQ2/+xKpCzs7O2ZgC2Zjq2kDq2kGa2kJC2kLa2tma2tpC2tra2ttu225C229u22/+2/7a2///bkDrbkGbbtmbbtpDbtrbb25Db27bb29vb2//b/7bb/9vb///e3t78pjb/tmb/25D/27b/29v//7b//9v///8rbMuDAAAACXBIWXMAAB2HAAAdhwGP5fFlAAAgAElEQVR4nO29C38cx9Wf2RAvmBCMJYMhN74oJFd5N9Gu8EK2N+t3xQimHFkJDYAi6vt/mEzP/Ur0DPqc/6nTz/OzRcwFPfXHqapnpi81TQEAgEHSqBsAAAAaEAAAwEBBAAAAAwUBAAAMFAQAADBQEAAAwEBBAAAAAwUBAAAMFAQAADBQEAAAwEBBAAAAAwUBAAAMFAQAADBQEAAAwEBBAAAAAwUBAAAMFAQAADBQEAAAwEBBAAAAAwUBAAAMFAQAADBQEAAAwEBBAAAAAwUBAAAMFAQAADBQEAAAwEBBAAAAAwUBAAAMFAQAADBQEAAAwEBBAFBOT9UtqIKLC3ULDLm+VrcAFCAAOD3FAB24uEhsgOtrDDBIEMDQOZ2z9xmXTz6Uy+bNzsc+vfjix89u/na08ov3Ptuam+bpkb95MafX9vTK8eGu5+x++O7PX+37zelDV835ka8MYhDAwDk9vc8AN+3cf7QAyuXKE+oVwMVFfAMcHe76+vMG+MyGpw8hgGpBAMPm9PQ+A3x60Q7x4wUw/f2uzzbm2Dny4qICAxwb7vr6HgPcKwCoFgQwaE5P7zXA1cl35SECmG2g67NtOXK+uriowQBHhru+vs8ACCAvCGDQ3C+ATy+efCgTAfz0rDn5+sPkzo/fjJrm+buymNI/ftM0zZfv2tuT6X66T+B21P7u7ejpcmP3Pnu56fHkcv5+1Dye6+Pjq/Fvnb0uGy0Y89PLpnm0eGC27Umb3z+b3yjjTZ28ns1XP7WbejQJM3uRf2kmMcdN2TWh3S+Ay+lOkMmvj1/39uXsb7X6c9Bw9wng7u14e5Pf3Gr+o2b60GwX0KGvDHoQwJA53WT7KbOxfdn8vpnPA+OpevLzyZv5lH6zvGP6UeFytmu4/e/d28VHgPufvbLp8STym/Gt6QyyeGSrBe0vTpi0c2Xb482eTW5MpHM5+fH3k1//vlluavYi/3OqpvFkt+OTzsUm209ZF8AfR/PNr/4cM9z1JptPWAhgu/mPX64K4NBXhgAggCFzvwDms/d4innyrn2jOZn0m68+lLs/tZPPZEofzwxf/lLuvm/Gz71p54LxM9qBP9tvtDxEeO+zVzc9fnAxQU7mkN+Ob/w0atuz9rTx9saPvJ//vNh22+anH2Ztvhm/Qy53l5PZafzzt+Mtvp88af4i07bu3kd1sACWf6t9f7c44e4VwHw/z87mrxwEPviVIQAIYMjcL4D50L2cvtW8aof1/PN8O+wnj8/vaN/JT3b43I7OxrPA/HeX+4nvffbqptsZ5buVhqzcWHva7MZkqlnd9vg/sznofD5Bjyfap4vJevq2dP4iN7M3rh32AN0vgOXfat/fLU64zgLY2fwVARz8yhAABDBk7hfAzex96mx0t7P0YpdOu9N+esfsA3775Lu3k3n8j6M3s5367fPm7//ue/baphevPWH8a49/WPy88rTV3Qtr257tWZpMXPMZdrEv+te//8vLZjpTTV9kaqvdx7oPFsDib7X37xYnXFcB7G7+xmmgB70yBAABDJp7DwHcLN51ToZwOwtM9wnPdkC3A3zx/nU65Y1vXH7x31+cL6aF5Q6A+569tun1OXK6M/zx//WhlLLRgtWzjNa2vZyU1z+MfHw5++2VmWq2B2r3roqDDwLP/1Z7/26BwnU8C2h381cFcPArgx4EMGg6HAPeEsCnF1sCmI3vyQ83zZtPL578r7dPF28Zl7PYfc9e2/TmjoP2pJcxX30oWy2YP2V92ytz5PxDyM38YObJ8z/+MNtXMXuR9nDEvl0VvQggariOAtjd/BUBHP7KoAcBDJrjBLC293rzPf3t6Hz8v3I5PwNkWwD7n7226e154+4v7XmG52WjBQe+SW73lc/eaq/OVO35rvt2VfQkgJjhOgtgV/OXAjjilUEPAhg2910GsNwFdN7+077ZXDunb2uv/ngCePJ+fPvmi/+2nAP2HgPYePb66YK73jje/alZ3zNePrebfNLmzd3k85/Hb2lXZ6pyefL/vN23q+Le68B2HAOYvDHf93eLFO6++X+xC2hH85cCOOaVQQ4CGDifn/8XB3JnJ51MJrirxf7f6UHg9dNTyuUX/3E8Fdz+u9/NJ4z1g8CfffbqptfnyPlGJg1ae9rV8nDo5oky54sHVk6Umc9UN830dJb5i7Snru/dVXHP/L+45GHlBJ2rzZ/Dhrtn/l/M8juavy2Ag14Z1CCAofPZ+X/lNNDJaefN7Hz2J+/K9Bz0lTP7f/1meurf9BTw8ZPmuww2TgP93LNXN70+R46nt/aRj29n+6OXT5ueKv/T/OfltlfnyPED5+0p9Iu9JON32xszVbuT+3zvn+nz838b43XbuOl8vDxFf/XnuOE+P//PjujubP70oeUuoENfGcQgANg7+5fVC8GmVwJP1gWeXZHa3ti6tncyHU33GMxPc9m4EOyzz17Z9MZekvnFso9/3Hja/GLZpyu/P7tY9nz68NPFkyYXy95Mn//kT9Mb8xcZt2F1L/cW+2f/Mj9HZroja/63Oi/rP0cOt3/2n7Wufde/o/nTh2aXBhz1yiAFAcBnuZkvBfFmst7M9M7pojDtdZ+bq/uUyYg/L4sZasdSEJ999nLTm7vJJ488WltV59vpI3uXy5lsc7bn5P2zxXI5s+dv7oq5WijrCO7Gr/v43c3sIHC7Ts7r5d/t0dbfra5wfxs1y3Wa1ps/eWiqeItXBmMQAHyW2WJwD+C2ll3Afa1qv3rSS5gTYHRL9vNlAZFBAPB5rh46hV1Wsgdg5ZPKw4gogN7CVfTK0AEEAJ/noR8BVr8QJjTv+7pcKaIAegtX0StDBxAA3MPNw+awyzpOAr9sejtWGU8APYar5pWhEwgA7uPyIR8B1r4UPjB/WhzhfjDxBNBjuGpeGTqBAAAABgoCAAAYKAgAAGCgIAAAgIGCAAAABgoCAAAYKAgAAGCgIAAAgIGCAAAABgoCAAAYKAgAAGCgIAAAgIGCAAAABgoCAAAYKAgAAGCgIAAAgIGCAAAABgoCAAAYKAgAAGCgIAAAgIGCAAAABgoCAAAYKAgAAGCgIAAAgIGCAAAABgoCAAAYKAgAAGCgIAAAgIGCAAAABgoCAAAYKAgAAGCgIAAAgIGCAAAABgoCAAAYKAgAAGCgIAAAgIGCAAAABgoCAAAYKAgAAGCgIAAAgIGCAAAABopAAP8KUCN0Y0jAejdWCMD/JQEeypYANM0AeAgIAOAYEAAkAAEAHAMCgAQgAIBjQACQAAQAcAwIABKAAACOAQFAAhAAwDEgAEgAAgA4BgQACUAAAMeAACABCADgGBAAJAABABwDAoAEIACAY0AAkAAEAHAMCAASgAAAjgEBQAIQAMAxIABIAAIAOAYEAAlAAADHgAAgAQgA4BgQACQAAQAcAwKABCCALFw2b25fNif/qZS/jZpH307u+/jNqGmev5s9/P5Z03z5TtrIRCCAXvj04uS78T9Xzfn4v7ejJx/aXtvMOyqd2hoEkIXL5o/jgdE055ftfyej6nZyR3PyZvLw2eTGFz+qG5oEBNAP40l88t+npdXA+L83a72WTm0LAsjCeIh89eHuT+Oh8bp8fNuOpE8vxveU8V3t+Bg//PRDeT+aDDd4OAigH27a9/7jrjrtpG/aCf7LX8rd95Ppnk5tDQLIwmXTfnweD5/z0o6q8Y3J+6ky+3g9fXj8z7muialAAP1wO3ra/udsPN9/ejGe1ee9dvKZgE5tDQLIwnQUzHaptmPl7u3kx9me1eln7MX4gYeCAPrh7u1k1v/j6M2ko969nb2dn0z3dGprEEAWdoyVZs54iM3eJTFW+gIB9MTluMdefvHfX5xP3tXPOnCZfhygU1uDALIwPZi2MlY+vWCsGIIAeuKmefPpxZP/9fbp5N39ZN5vmQmATm0LAsjCjrEyey81e/i8/Yex0hcIoCduR+fj/5XLJ/+znfI3PwHQqW1BAFnYGiuLvamzh8/bfxgrfYEAeuLu7ZP3455688V/a/vm5jEAOrUtCCALW2NlPC4m50isHk5jrPQGAuiLyy/+47jT3v6730168MZZQHRqWxBAFrbHyqcXzZN3ZXaaNGOlZxBAX9w0s946v9Dry1/Kr9/MrgOgU9uCALKwPVbm11Q2XxXGSu8ggL4YT/nTfT+z9/Zrl/rSqW1BAFnYMVZmy6Z8O334vP2HsdIXCKAvxlP/eVm5nGt9LaBCp7YEAQAcAwKABCAAgGNAAJAABABwDAgAEoAAAI4BAUACEADAMSAASAACADgGBAAJQAAAx4AAIAEIAOAYEAAkAAEAHAMCgAQgAIBjQACQAAQAcAwIABKAAACOAQFAAiIIAKBG6MaQgPVujAAAukE3hgSsd+NB7gL6p7oBZqRNpg8WbxeQ/m9iRdpk+mAIoEQogxVpk+mDIQA/0ibTB0MAJUIZrEibTB8MAfiRNpk+GAIoEcpgRdpk+mAIwI+0yfTBEECJUAYr0ibTB0MAfqRNpg+GAEqEMliRNpk+GALwI20yfTAEUCKUwYq0yfTBEIAfaZPpgyGAEqEMVqRNpg+GAPxIm0wfDAGUCGWwIm0yfTAE4EfaZPpgCKBEKIMVaZPpgyEAP9Im0wdDACVCGaxIm0wfDAH4kTaZPhgCKBHKYEXaZPpgCMCPtMn0wRBAiVAGK9Im0wdDAH6kTaYPhgBKhDJYkTaZPhgC8CNtMn0wBFAilMGKtMn0wRCAH2mT6YMhgBKhDFakTaYPhgD8SJtMHwwBlAhlsCJtMn0wBOBH2mT6YAigRCiDFWmT6YMhAD/SJtMHQwAlQhmsSJtMHwwB+JE2mT4YAigRymBF2mT6YAjAj7TJ9MEQQIlQBivSJtMHQwB+pE2mD4YASoQyWJE2mT4YAvAjbTJ9MARQIpTBirTJ9MEQgB9pk+mDIYASoQxWpE2mD4YA/EibTB8MAZQIZbAibTJ9MATgR9pk+mAIoEQogxVpk+mDIQA/0ibTB0MAJUIZrEibTB8MAfiRNpk+GAIoEcpgRdpk+mAIwI+0yfTBEECJUAYr0ibTB0MAfqRNpg+GAEqEMliRNpk+GALwI20yfTAEUCKUwYq0yfTBEIAfaZPpgyGAEqEMVqRNpg+GAPxIm0wfDAGUCGWwIm0yfTAE4EfaZPpgCKBEKIMVaZPpgyEAP9Im0wdDACVCGaxIm0wfLLoArq9FzTBAX20j9MEQQIlQBivSJtMHQwB+6KtthD4YAigRymBF2mT6YAjAD321jdAHQwAlQhmsSJtMHwwB+KGvthH6YH0I4OOrpmmev9vxyN3bL35cPOubUdM8ev1huwlHvGS/6MtgRdpk+mAIwA99tY3QB+tBADfNhJM32w9dNQsBvB9Nn/ZkywDRRk4m0ibTB0MAfuirbYQ+2MMFcDtqnrwrH982J99tPnTVLAQwtsTjd+XuT03zdKsJB79k3+jLYEXaZPpgCMAPfbWN0Ad7uAAup2/q795uTu133zcLAYwfnb71X/lMsGjCwS/ZN/oyWJE2mT4YAvBDX20j9MEeLIBPL5rpvp+bjan9p5dNcza/72b++WD8eWFzV1G0kZOJtMn0wRCAH/pqG6EP9mABLOb9hQnK/ObJ68WDlzv2/S+acOhL9o6+DFakTaYPhgD80FfbCH2wBwvgaj61371tzlfu//Tiqw8LO2w8ttGEQ1+yd/RlsCJtMn0wBOCHvtpG6IM9WACXKwJYOwjwS1l+PGg/Hdy9f9aeBrqjCYe+ZO/oy2BF2mT6YAjAD321jdAH60EAT7d+WjAXwO2oef2W00D9SZtMHwwB+KGvthH6YG4CaJqvPkxODFo86V/n/BOgOrYEoG7QBtfX6hZABfgJ4Hxyz9X25QLR3jplIm0yfTA+Afihr7YR+mBuApjt+vn0YutwcLSRk4m0yfTBEIAf+moboQ9mdxC4ZUUAs8d2PCvayMlE2mT6YAjAD321jdAHMzsNdMLKWUDzx7Y/J0QbOZlIm0wfDAH4oa+2EfpgZheCrT24fN/PJwBX0ibTB0MAfuirbYQ+mN1SEGv3Xa1o4nyzCYe+ZO/oy2BF2mT6YAjAD321jdAHM1wMrqwIYHEQ4JLF4DxJm0wfDAH4oa+2EfpgpstBr3wquGqaL3+ZXAdwvtWEg1+yb/RlsCJtMn0wBOCHvtpG6IP18IUw79e+EOamWTkUsLJb6Pvps5qvtptw+Ev2jL4MVqRNpg+GAPzQV9sIfbDevxJynwDKz5NnfbujCUe8ZL/oy2BF2mT6YAjAD321jdAH40vhS4QyWJE2mT4YAvBDX20j9MEQQIlQBivSJtMHQwB+6KtthD4YAigRymBF2mT6YAjAD321jdAHQwAlQhmsSJtMHwwB+KGvthH6YAigRCiDFWmT6YMhAD/01TZCHwwBlAhlsCJtMn0wBOCHvtpG6IMhgBKhDFakTaYPhgD80FfbCH0wBFAilMGKtMn0wRCAH/pqG6EPhgBKhDJYkTaZPhgC8ENfbSP0wRBAiVAGK9Im0wdDAH7oq22EPhgCKBHKYEXaZPpgCMAPfbWN0AdDACVCGaxIm0wfDAH4oa+2EfpgCKBEKIMVaZPpgyEAP/TVNkIfDAGUCGWwIm0yfTAE4Ie+2kbogyGAEqEMVqRNpg+GAPzQV9sIfTAEUCKUwYq0yfTBEIAf+moboQ+GAEqEMliRNpk+GALwQ19tI/TBEECJUAYr0ibTB0MAfuirbYQ+GAIoEcpgRdpk+mAIwA99tY3QB0MAJUIZrEibTB8MAfihr7YR+mAIoEQogxVpk+mDIQA/9NU2Qh8MAZQIZbAibTJ9MATgh77aRuiDIYASoQxWpE2mD4YA/NBX2wh9MARQIpTBirTJ9MEQgB/6ahuhD4YASoQyWJE2mT4YAvBDX20j9MEQQIlQBivSJtMHQwB+6KtthD4YAigRymBF2mT6YAjAD321jdAHQwAlQhmsSJtMHwwB+KGvthH6YAigRCiDFWmT6YMhAD/01TZCHwwBlAhlsCJtMn0wBOCHvtpG6IMhgBKhDFakTaYPhgD80FfbCH0wBFAilMGKtMn0wRCAH/pqG6EPhgBKhDJYkTaZPhgC8ENfbSP0wRBAiVAGK9Im0wdDAH7oq22EPhgCKBHKYEXaZPpgCMAPfbWN0AdDACVCGaxIm0wfDAH4oa+2EfpgCKBEKIMVaZPpgyEAP/TVNkIfDAGUCGWwIm0yfTAE4Ie+2kbogyGAEqEMVqRNpg+GAPzQV9sIfTAEUCKUwYq0yfTBEIAf+moboQ+GAEqEMliRNpk+GALwQ19tI/TBEECJUAYr0ibTB0MAfuirbYQ+GAIoEcpgRdpk+mAIwA99tY3QB0MAJUIZrEibTB8MAfihr7YR+mAIoEQogxVpk+mDIQA/9NU2Qh8MAZQIZbAibTJ9MATgh77aRuiDIYASoQxWpE2mD4YA/NBX2wh9MARQIpTBirTJ9MEQgB/6ahuhD4YASoQyWJE2mT4YAvBDX20j9MEQQIlQBivSJtMHQwB+6KtthD4YAigRymBF2mT6YAjAD321jdAHQwAlQhmsSJtMHwwB+KGvthH6YAigRCiDFWmT6YMhAD/01TZCHwwBlAhlsCJtMn0wBODHWrK0wSQggBKhDFakTaYPhgD8QABmIIASoQxWpE2mD4YA/EAAZiCAEqEMVqRNpg+GAPxAAGYggBKhDFakTaYPhgD8QABmIIASoQxWpE2mD4YA/EAAZiCAEqEMVqRNpg+GAPxAAGYggBKhDFakTaYPhgD8QABmIIASoQxWpE2mD4YA/EAAZiCAEqEMVqRNpg+GAPxAAGYggBKhDFakTaYPFlwA19eJJkoEYAYCKBHKYEXaZPpgoQVwPUfXmj5BAGYggBKhDFakTaYPFlkA19e5DIAAzEAAJUIZrEibTB8ssACur5MZAAGYgQBKhDJYkTaZPlhcAVxfZzMAAjADAZQIZbAibTJ9MATgBwIwAwGUCGWwIm0yfbCwArjeRNuqPkAAZiCAEqEMVqRNpg+GAPxAAGYggBKhDFakTaYPhgD8QABmIIASoQxWpE2mD4YA/EAAZiCAEqEMVqRNpg8WVgAcBK6HeN0YAaQibTJ9MATgBwIwAwGUCGWwIm0yfTAE4AcCMAMBlAhlsCJtMn2wuALgSuBqiNeNEUAq0ibTBwssANYCqoV43RgBpCJtMn2wyAJgNdBKiNeNEUAq0ibTBwstAL4PoA7idWMEkIq0yfTBggsg7zyZNpgEBFAilMGKtMn0wRCAHwjADARQIpTBirTJ9MEQgB8IwAwEUCKUwYq0yfTBEIAfCMAMBFAilMGKtMn0wRCAHwjADARQIpTBirTJ9MEQgB8IwAwEUCKUwYq0yfTBEIAfCMAMBFAilMGKtMn0wRCAHwjADARQIpTBirTJ9MEQgB8IwAwEUCKUwYq0yfTBEIAfCMAMBFAilMGKtMn0wRCAHwjADARQIpTBirTJ9MEQgB8IwAwEUCKUwYq0yfTBEIAfCMAMBFAilMGKtMn0wRCAHwjADARQIpTBirTJ9MEQgB8IwAwEUCKUwYq0yfTBEIAfCMAMBFAilMGKtMn0wRCAHwjADARQIpTBirTJ9MEQgB8IwAwEUCKUwYq0yfTBEIAfCMAMBFAilMGKtMn0wRCAHwjADARQIpTBirTJ9MEQgB8IwAwEUCKUwYq0yfTBEIAfCMAMBFAilMGKtMn0wRCAHwjADARQIpTBirTJ9MEQgB8IwAwEUCKUwYq0yfTBEIAfq8murxMli9eNEUAq0ibTB0MAfiyTXc8RtqZH4nVjBJCKtMn0wRCAH4tk19e5DBCvGyOAVKRNpg+GAPyYJ7u+TmaAeN0YAaQibTJ9MATgxyzZ9XU2A8TrxgggFWmT6YMhAD8QgBkIoEQogxVpk+mDIQA/psmuNxG3qgfidWMEkIq0yfTBEIAfCMAMBFAilMGKtMn0wRCAHwjADARQIpTBirTJ9MEQgB8IwAwEUCKUwYq0yfTBEIAfHAQ2AwGUCGWwIm0yfTAE4AcCMAMBlAhlsCJtMn0wBOAHAjADAZQIZbAibTJ9MATgB1cCm4EASoQyWJE2mT4YAvCDtYDMQAAlQhmsSJtMHwwB+MFqoGYggBKhDFakTaYPhgD84PsAzEAAJUIZrEibTB8MAfjBN4KZgQBKhDJYkTaZPhgC8AMBmIEASoQyWJE2mT4YAvADAZiBAEqEMliRNpk+GALwAwGYgQBKhDJYkTaZPhgC8AMBmIEASoQyWJE2mT4YAvADAZiBAEqEMliRNpk+GALwAwGYgQBKhDJYkTaZPhgC8AMBmIEASoQyWJE2mT4YAvADAZiBAEqEMliRNpk+GALwAwGYgQBKhDJYkTaZPhgC8AMBmIEASoQyWJE2mT4YAvADAZiBAEqEMliRNpk+GALwAwGYgQBKhDJYkTaZPhgC8AMBmIEASoQyWJE2mT4YAvADAZiBAEqEMliRNpk+GALwAwGYgQBKhDJYkTaZPhgC8AMBmIEASoQyWJE2mT4YAvADAZiBAEqEMliRNpk+GALwAwGYgQBKhDJYkTaZPhgC8AMBmIEASoQyWJE2mT4YAvADAZiBAEqEMliRNpk+GALwAwGYgQBKhDJYkTaZPhgC8AMBmIEASoQyWJE2mT4YAvADAZiBAEqEMliRNpk+GALwAwGYgQBKhDJYkTaZPhgC8AMBmIEASoQyWJE2mT4YAvADAZiBAEqEMliRNpk+GALwAwGYgQBKhDJYkTaZPhgC8AMBmIEASoQyWJE2mT4YAvBjIAJQJEMAJcJsYkXaZPpgCMAPBGAGAigRZhMr0ibTB0MAfiAAMxBAiTCbWJE2mT4YAvADAZiBAEqE2cSKtMn0wRCAHwjADARQIswmVqRNpg+GAPxAAGYggBJhNrEibTJ9MATgBwIwAwGUCLOJFWmT6YMhAD8QgBkIoESYTaxIm0wfDAH4gQDMQAAlwmxiRdpk+mAIwA8EYAYCKBFmEyvSJtMHQwB+IAAzEECJMJtYkTaZPhgC8AMBmIEASoTZxIq0yfTBEIAfCMAMBFAizCZWpE2mD4YA/EAAZiCAEmE2sSJtMn0wBOAHAjADAZQIs4kVaZPpgyEAPxCAGQigRJhNrEibTB8MAfiBAMxAACXCbGJF2mT6YNEFkAkEYAYCKIMZOZnQB0MAfiAAMxBAGczIyYQ+GALwAwGYgQDKYEZOJvTBEIAfCMAMBFAGM3IyoQ+GAPxImwwBFEaOJWmT6YMhAD/SJsshgI+vmqZ5/m7HI3dvv/hx9uOvf36251mMHDvSJtMHQwB+pE2WQgA3zYSTN9sPXTVzAdyMps9qzrebcPhL9kza/pU3mT4YAvAjbbIMArgdNU/elY9vm5PvNh+6auYCGD/p8Q+lfVaz5QlGjh1pk+mDIQA/0ibLIIDL5smH0u7taZ6uP3D3fbMQwPyjwPhZk2evNeHgl+ybtP0rbzJ9MATgR9pkCQTw6cXsPf3NYnfPlJ9eNs3Zct6f2eFm+4MCI8eOtMn0wRCAH2mTJRDAYt5fmKDMb5683pRCuy8IATiSNpk+GALwI22yBAK4mu/TGb/LP1+5/9OLrz5sfSrgE4AzaZPpgyEAP9ImSyCAyxUBrB0E+KVs7xZqn755DyPHkLTJ9MEQgB9pk6UQwNOtnxZsCeB2tHzSv875J0B1bAlA3SConetr/9f0FcD4U8LWBwDeOhmSNpk+GJ8A/EibbGCfAO52XQbAyDEkbTJ9MATgR9pkwxJAO/+f72jCoS/ZO2n7V95k+mAIwI+0yVIIYM9B4JY1AeyZ/xk5hqRNpg+GAPxIm6wWAfyyfwP7TgOdsCqATy+b5vXOJtzbSGvS9q+8yfTBEIAfaZNVIoBPLx59vbmAw5x9F4KtP1jKxxc7l4srjBxL0ibTB0MAfqRNVo0A2pWcv925gb1LQazf114YvLVY3KwJXdtqRtr+lTeZPhgC8CNtsvlXn/sAACAASURBVJoEMObLXWv+710MrqwI4G7XYqHzJhzQXBvS9q+8yfTBEIAfaZNVIoC7P89X8z/5eutwwGeWg14K4GrX+Z/zJhzUYAvS9q+8yfTBEIAfaZNVIoDSfqHX3AGPXm8cDni/9oUwN6tT/VwA848QO78ShpFjR9pk+mAIwI+0yeoRQJl/qWOzfThg7SshdwrgdoQAJKRNpg+GAPxIm6wqAZR2X9DMAY/37dA/pgn9bepI0vavvMn0wRCAH2mTVSaAxfzf7D+ke0QTetvSsaTtX3mT6YMhAD/SJqtJAMvZ/6TdobN9ys/RTehtS8eStn/lTaYPhgD8SJusGgEs9/+ffPmu3F02O076P7oJfW3oaNL2r7zJ9MEQgB9pk1UigOVJPM9/mNxxO0IAVZA2mT4YAvAjbbK6BPD42w/LOxBADaRNpg+GAPxIm6wiAaytBvTp1W/2Lg50eBP62tDRpO1feZPpgyEAP9Imq0UA/2H7AuAem2C36Y6k7V95k+mDIQA/0iarRAB3//n3f5i/4b999eyrnpvQ7+aOIG3/yptMHwwB+JE2WSUCWN3lP/75SW97f6ZN6HVrx5C2f+VNpg+GAPxIm6xCAfR6AtC0Cb1u7RjS9q+8yfTBEIAfaZNVIIDLZgsEUA1pk+mDIQA/0iarQABr67hNYRdQNaRNpg+GAPxIm6wCAez4CLB3Zf8jm9Dv5o4gbf/Km0wfDAH4kTZZDQK4fXXWLgPx6GzK8z/s+lqwBzWh5+0dTtr+lTeZPhgC8CNtshoEUHq+8He7CWZb7kra/pU3mT4YAvAjbbJKBLB6HYBBE8y23JW0/StvMn0wBOBH2mSVCMC4Cf4vuUHa/pU3mT4YAvAjbTIEUBg5lqRNpg+GAPxImyy+AD69Onv+4/g/qzznOoBaSJtMHwwB+JE2WQUCaI//Lr8PgAvB6iJtMn0wBOBH2mQIoDByLEmbTB8MAfiRNhkCKIwcS9Im0wdDAH6kTRZfAB5N8H/JDdL2r7zJ9MEQgB9pkyGAwsixJG0yfTAE4EfaZAigMHIsSZtMHwwB+JE2GQIojBxL0ibTB0MAfqRNFl8AG5cAcB1AXaRNpg+GAPxIm6wCAayfAMRZQHWRNpk+GALwI20yBFAYOZakTaYPhgD8SJsMARRGjiVpk+mDIQA/0iaLLwCPJvi/5AZp+1feZPpgCMCPtMkQQGHkWJI2mT4YAvAjbTIEUBg5lqRNpg+GAPxImwwBFEaOJWmT6YMhAD/SJosvAL4PoGrSJtMHQwB+pE22Fuz6WmAAVgMtiftX3mT6YAjAj7TJVoJdz/FtAQIoiftX3mT6YAjAj7TJlsGurzUGQAAlcf/Km0wfDAH4kTbZItj1tcgAHAQuiftX3mT6YAjAj7TJ5sGur1UGQAAlcf/Km0wfDAH4kTYZAiiMHEvSJtMHQwB+pE02C3a9iV8Lugvg17+O+bcPBk3of5MHkrZ/5U2mD4YA/EibrB4B/PRydgT48bvem9D3Bg8mbf/Km0wfDAH4kTZZNQK4WjkJ6HXfTeh5e4eTtn/lTaYPhgD8SJusFgHcrJ4FevJdz03od3NHkLZ/5U2mD4YA/EibrJaDwJfjef/R1z/84+e/tHuCnvbchH43dwT6FTmsSD9ydCAAP9Imq0QA7YVg81n/Mv2FYAigAvTBEIAfaZPVI4DFfp/JpcH9NqHXrR0DAqgOfTAE4EfaZJVcCXz3djnpjwXwpN9zQaONHARQAfpgCMCPtMnirwU05app3sx+vOEYQEUMYOSoQAB+pE0WfzXQKeOPALN9QO9Hfe8BCjdyEEAF6IMhAD/SJqvg+wCm3wXzrD398/nvztp/vu75cuBoIwcBVIA+GALwI20y/czTYTnoLTgIXA0DGTkKEIAfaZPpZx4EUCKUwYqBjBwFCMCPtMn0Mw8CKBHKYMVARo4CBOBH2mT6mYfloEuEMlgxkJGjAAH4kTaZfuZBACVCGawYyMhRgAD8SJtMP/MggBKhDFYMZOQoQAB+pE2mn3kOF8DdT/8HxwBqYSAjRwEC8CNtMv3M01EAH19xELhGBjJyFCAAP9Im08883QSwfi4QAqiGgYwcBQjAj7TJ9DNP57WAEECNDGTkKEAAfqRNpp95uq4G2jSPno3/fzaAr4REABWgD4YA/EibTD/zdP5CmPG0f3ny3drK0H01od/NHYG+DFYMZOQoQAB+pE2mn3m6CqCd9a+a81JuRywHXQ8DGTkKEIAfaZPpZ55DBHDTTv18IUxNDGTkKEAAfqRNpp95Oh8DOG/f/I81cMNB4IoYyMhRgAD8SJtMP/N0Pwvoix/HHnj0uxECqIiBjBwFCMCPtMn0M0/36wDG0/7sbFCOAVTDQEaOAgTgR9pk+pmn45XAd9+0ApheDzb7csj+mtDv5o5AXwYrBjJyFIQTwMWFugVm6KtthH7m6bwW0K//74dSPr5smsc9z//6kROgDFYMZOQoQAB+6KtthH7mOXgxuH7PAJo0ofctHoq+DFYMZOQoQAB+6KtthH7mYTnoEqEMVgxk5ChAAH7oq22EfubpLoBf/zrm3/r/AKAfOQHKYMVARo4CBOCHvtpG6GeergL46eVsJbjH73pvQt8bPBh9GawYyMhRgAD80FfbCP3M01EAq8uBshhcPQxk5ChAAH7oq22EfubpJoCb1dWgOQ20HgYychQgAD/01TZCP/N0E8Bluxz01z/84+e/vORCsJoYyMhRgAD80FfbCP3M03k56Pmsf8lSEBUxkJGjAAH4oa+2EfqZp6sAFvt9ZiuD9tmEXrd2DPoyWDGQkaMAAfihr7YR+pmn62qgi0mf5aBrYiAjRwEC8ENfbSP0M0/n1UDfzH684RhARQxk5ChAAH7oq22EfubpJoDxR4DZPqD3I74SsiLWkqUNJgEB+KGvthH6mec+AXx6ddbyrD398/nv2u+EP/m658uB5SMnQBmsQABmIAA/9NU2Qj/z3CuAF80WHASuBgRgBgLwQ19tI/QzDwIoEcpgBQIwAwH4oa+2EfqZBwGUCGWwAgGYgQD80FfbCP3Mw3LQJUIZrEAAZiAAP/TVNkI/8yCAEqEMViAAMxCAH/pqG6GfeRBAiVAGKxCAGQjAD321jdDPPAcI4Oe//vWvP1g0wWCbh6EvgxUIwAwE4Ie+2kboZ56uArj782i6FvR/6r8JvW/xUPRlsAIBmIEA/NBX2wj9zNNRAHdvF+cA9bwSUICRE6AMViAAMxCAH/pqG6GfeToK4HLlLNCelwLSj5wAZbACAZiBAPzQV9sI/czT/RvBTr7+4R9//5d2R9Cbnc85vgn9bu4I9GWwAgGYgQD80FfbCP3M0/kbwWZ7ftp9QawGWg0IwAwE4Ie+2kboZ56u3wew+EKY2xHfB1APCMAMBOCHvtpG6Geert8ItvqFMCwFUQ0IwAwE4Ie+2kboZx4EUCKUwQoEYAYC8ENfbSP0M0/XXUDN+eznq95PBJWPnABlsAIBmIEA/NBX2wj9zNP5KyGb37bT/t2fmqUL+mpCv5s7An0ZrEAAZiAAP/TVNkI/83QTwHRR6EeTLwbjKyErAgGYgQD80FfbCP3M0/FCsJuVC8F6vgxAP3IClMEKBGAGAvBDX20j9DNP17WA3o9m0//J696b0PcGD0ZfBisQgBkIwA99tY3QzzydVwO9+3O7/+fs675XAgowcgKUwQoEYAYC8ENfbSP0Mw/fB1AilMEKBGAGAvBDX20j9DNPNwH8zeCN/7IJdpvuiL4MViAAMxCAH/pqG6GfebpeB9D7ItArTTDbclf0ZbACAZiBAPzQV9sI/cxz8JXABk0w23JX9GWwAgGYgQD80FfbCP3Mc/BicAZNMNtyV/RlsAIBmIEA/NBX2wj9zNP5+wAevzNrgtWGO6MvgxUIwAwE4Ie+2kboZ55unwD+/s30SuApz1kMrhYQgBkIwA99tY3QzzxdjwGswmqg1YAAzEAAfuirbYR+5kEAJUIZrEAAZiAAP/TVNkI/8yCAEqEMViAAMxCAH/pqG6GfebgSuEQogxUIwAwE4Ie+2kboZx4EULamyUQTJQIwAwH4oa+2EQigBBg5q2W4nqNrTZ8gADMQgB/6ahtRkwDu/v7Xv/713wxWhJCPnJUyXF/nMgACMAMB+KGvthH1CODjy9kR4P4vCJOPnGUZrq+TGWC1g2XJNEE/JSAAP/TVNqIaAbxfngN0kvcbwa6vsxlgx2cbYWt6RD8lIAA/9NU2ohYB3I5WzgLte1kg+cgZggByxYowJSAAP/TVNqIWAVy2C0F8/cM/fv5Luyfoac9N6HdzRzArw/Um2lb1wU61SVvUE/opAQH4oa+2EZUIoL0Q7Ons8O9l3gvB8gogXa4AUwIC8ENfbSPqEcBiv8/tCAHUAwIwAwH4oa+2EZUI4O7tctIfy6DnbweTj5z0AkgbTAkC8ENfbSMqEUC732d+7s9N05z33IR+N3cE2Q8CIwADEIAf+mobUYsAbkfNybeTn96P+j4GrB85CKA+9FMCAvBDX20jKhHA3d//PGqak7PfnS2/F6a/b4WRjxwEUB/6KQEB+KGvthH6YMcsB93zmtDykZP/SuB083/AkSPvxgigPvTBEEAZwFpACKB/EIAf+moboQ+GAMoAVgNFAP2DAPzQV9sIfTCWgy5D+D6AbPN/wJEj78YIoD70wRBAiXAs3grWAjIDAfihr7YR+mAIoAxCAEn3bQlBAH7oq22EPhgCKMMQQNJ9WzoQgB/6ahuhD4YAylAEkDeYBATgh77aRuiDIYCCACok3siRd2MEUB/6YAigIIAKiTdy5N0YAdSHPhgCKAigQuKNHHk3RgD1oQ+GAAoCqJB4I0fejRFAfeiDIYCCACpEXzIE4Id+njRCHwwBlAiziRUIwAwE4Id+njRCHwwBlAiziRUIwAwE4Id+njRCH+w+AXx6dbZFf18FMG1Cr1s7Bv1sYgUCMAMB+KGfJ43QB7tXAKYLgU6b0OvWjkE/m1iBAMyIJoCLi7wG0M+TRuiDIYASYTaxAgGYEUsAF3OkrbBCP08aoQ92uABOfvOHD/02odetHYN+NrECAZgRSgAXF6kNoJ8njdAH63gQ+KppfvvL+N+775vmSb/zPwIwBAGYEUkAFxe5DaCfJ43QB+smgJumOZ/9eLX8sa8m9Lu5I9DPJlYgADMCCeDiIrkB9POkEfpgnQRw93a53//Ti74/AiAAOxCAGQjAD/08aYQ+WNfvBF4VAAeBqwEBmBFHABebyFpihX6eNEIfrPOXwr+Z/XzDWUAVgQDMQAB+6OdJI/TBuu4Cak5eT358P+r9KDACsAMBmIEA/NDPk0bog3U7CHw1OQH07Oxs8u95z03od3NHoJ9NrEAAZiAAP/TzpBH6YN0E0H4EWMBpoPWAAMyIIwAOAleLPljH6wBWDPBVz/M/AjAEAZiBAPzQz5NG6IN1Xg3052+ejWf/R1++678JvW/xUPSziRUIwAwE4Id+njRCH4zloEuE2cQKBGBGIAFwJXCt6IMhgBJhNrECAZgRSQCsBVQp+mCdBXD3l1ej9gqAT/+l9yb0vcGD0c8mViAAM0IJgNVA60QfrKsA/jaaLQR90/dZoAjAEARgRiwB8H0AVaIP1n010JkALpuT73puQr+bOwL9bGIFAjAjmgD4RrAK0QfrvBpo0zxrBdCeD/q05yb0u7kj0M8mViAAMxCAH/p50gh9sG4CuGyaJ++my8BdsRRERSAAMxCAH/p50gh9sK5rAZ18N1sHlNVAawIBmIEA/NDPk0bogx2wHDQCqA8EYAYC8EM/TxqhD9ZVAItPALcjBFAPCMAMBOCHfp40Qh+s83LQTz5MBMBB4KpAAGYgAD/086QR+mAHnAV0NmpO/n37w5udzzm+Cf1u7gj0s4kVCMAMBOCHfp40Qh+s43UAlywHXSUIwAwE4Id+njRCH6zrctDff2b+//hqfPfzrWVCN+7+aXLzhx1NOLTNvaOfTaxAAGYgAD/086QR+mDdl4N+NZn+n3+79cjNVAwnbz5799wg59tNOLjRfaOfTaxAAGYgAD/086QR+mAHrQb6j1133o6aJ+/Kx7cbS0Rs3H0z+SaZ8c3tIwjykRNgNrFC38GM0JcMAfgxkG6soNtZQP/593+Y7/i5ffXsq7UHL6c7he7erp8dtH73/NHxv1u7kOQjJ8BsYoW+gxmhLxkC8GMg3VjBAReCLX5em8LHt6fv6W/Wrg/YuHtx82r7KgL5yAkwm1ih72BG6EuGAPwYSDdWcLAANi8EW8z7izl+193j/55PbiIAV/QdzAh9yRCAHwPpxgruFcDqCaDNYlHoJVfzDwR3b1cP8G7cPd/1s7mjaNKEh2Z4MPrZxAp9BzNCXzIE4MdAurGCewVwO9oSwNouoMuVmf7p/rtvRu1B4F//zx3fJiAfOQFmEyv0HcwIfckQgB8D6cYK7t8FtP0R4M36w0+3ftp19+3Lye8+3rpcQD9yAswmVug7mBH6kiEAPwbSjRXcL4DbV2fPxjP3o7Mpz/+wPoV3FcD0OyWbk9crLz3jn7G4vla3AA5EUbItAfg3YZ2LC3ULoD4OPgi8SUcBjD9H/PbD5ILi880tyN86BXg7aYX+HYYR+pIF+ARwerp6i08A9aEPdvB1AJt0E8BNszgLaOsgAAKwQ9/BjNCXDAG4kTeZfnwedCXwLrodBL5cOSv0fLMJh75k7+hnEyv0HcwIfckQgBt5k+nHZ1cBvD+bvnG/HX21/lmg22mgiw8EO84DRQB26DuYEfqSIQA38ibTj8+OArha7sLZmMC7XQiGADToO5gR+pIhADfyJtOPz24CaC8GmK/ls3EaaLelIK7YBSRB38GM0JcMAbiRN5l+fHYTwOX88q/J9wI83Xisw2JwY4OcT+7mILAr+g5mhL5kCMCNvMn047PzdwKfz36+2fxKmG7LQV9OloNu/cFSEI7oO5gR+pIhADfyJtOPz2NWA924JuD92je/3Mx3Ea3fPdl31PLV1vmkCMAOfQczQl8yBOBG3mT68dmDANa/+3EhgN1fCbn9hWIIwBB9BzNCXzIE4EbeZPrx2XkX0PzI79YuoIc3odetHYN+NrFC38GM0JcMAbiRN5l+fHY+CDzbw//TaMde/Ac2od/NHYF+NrFC38GM0JcMAbiRN5l+fHYTwOQL3k/Ozs62VwPtoQn9bu4I9LOJFfoOZoS+ZAjAjbzJ9OOz44Vgq2tC9/wBAAEYou9gRuhLhgDcyJtMPz47CmBy/v/sNJ7em9D3Bg9GP5tYoe9gRuhLhgDcyJtMPz47Lwb38ZvJtwJ8uf2FLg9uQu9bPBT9bGKFvoMZoS8ZAnAjbzL9+HzwaqA9NMH/JTfQzyZW6DuYEfqSIQA38ibTj08EUCLMJlboO5gR+pIhADfyJtOPTwRQIswmVug7mBFrwa6vBTVDAG7kTaYfn/cJ4NOrs+c/jv+zyvN9Xw95ZBN63doxIIDqWAl2Pce3BQjAjbzJ9OPzXgG0Kz+M/7PK3u8HPrIJvW7tGBBAdSyDXV9rDIAA3MibTD8+EUBBABWyCHZ9LTIAAnAjbzL9+BymANZHDgKoj3mw62uVARCAG3mT6cfnMA8Cf1YAmUibDAEUBFA/+vGJAEqEMliRNtks2PUmfi1AAG7kTaYfnwigRCiDFWmTIYCCAOpHPz4RQIlQBivSJkMABQHUj358drgOYIv6rwNAALWDAAoCqB/9+OxwFtAWyc8CykTaZBwELgigfvTjEwGUCGWwIm0yBFAQQP3ox+fhAjj5zR+q/05gBFA7CKAggPrRj8+OB4Gvmua3v5TpF8P0/J3w+pEToAxWpE3GlcBlMAK4uEgbTT8+O38n8Pnsx6vlj301od/NdQEB1A5rAZWBCOBijrohFujHZycB3L1d7vf/9KLvjwDykROgDFakTcZqoGUYAri4yGwA/fjsJIDJgkA7fu6pCb1urRMIoHb4PoAyCAFcXKQ2gH58dhVA82b28w1nAVVE2mT69fsQgAMXF7kNoB+fXXcBNSevJz++H/V+FFg+cgKUwYq0yRBAQQD1ox+f3Q4CX01OAD07O5v8e95zE/rdXBcQQO0ggDIAAVxsom5Q3+jHZzcBtB8BFnAaaD2kTYYACgKoH/347HgdwIoBvup5/tePnABlsCJtMgRQEED96Mdn59VAf/7m2Xj2f/Tlu/6b0PsW7wUB1A4CKAigfvTjk+WgS4QyWJE2GQIoAxAAB4HNQQAlQhmsSJsMARQEUD/68dlZAHd/eTVqrwD49F96b0LfG7wfBFA7CKAggPrRj8+uAvjbaLYQ9E3fZ4HqR06AMliRNhkCKEMQAFcCW9N9NdCZAC6bk+96bkK/m+sCAqgdBFAGIQDWAjKm82qgTfOsFUB7PujTnpvQ7+a6gABqBwGUYQiA1UBt6SaAy6Z58m66DNwVS0FURNpkCKAMRAB8H4ApXdcCOvlutg4oq4HWRNpkCKAMRgCJk+nH5wHLQSOA+kibDAEUBFA/+vHZVQCLTwC3IwRQD2mTIYCCAOpHPz47Lwf95MNEABwEroq0yRBAQQD1ox+fB5wFdDZqTv59+8Obnc85vgn9bq4LCKB2EEBBAPWjH58drwO4ZDnoKkmbDAEUBFA/+vHZdTno783mf/3ICVAGK9ImQwAFAdSPfnx2Xw761WT6f/5t/03ofYv3ggBqBwGU4XRjBGDHQauB/sOmCSZb/SxDGTl5kyGAMpxujADs6CaAv33d936f1SbYbXofQ/nsHKCDGYEACgKoH33Jup4G2vue/5UmmG15LwigdhBAQQD1oy/ZAVcCmzXBbMt7QQC1gwAKAqgffckOWAvIrAlmW94LAqgdBFAQQP3oS9b5QrDH/X8b/LwJVhveDwKoHQRQEED96EvW7RPA379pTwF9dDblebK1gPL2rwAdzAgEUBBA/ehL1vUYwCrZFoPL278CdDAjEEBBAPWjLxkCyNy/AnQwIxBAQQD1oy8ZAsjcvwJ0MCMQQEEA9aMv2UFXAhs1wf8lEUDtIICCAOpHXzIEkLl/BehgRiCAggDqR18yBJC5fwXoYEYggIIA6kdfsg4CuPvp1dnZb/pfBXTRBLMt7wUB1A4CKAigfvQlu18AH18afQ/AoglG2/0MCKB2EEBBAPWjL9m9AlieAWRlAPnIydu/AnQwIxBAQQD1oy/ZvQK4Wp7+2fN3AS+aYLPZz4EAagcBFARQP/qS3SeAu7ftif8//I/2c8BToybYbPZzIIDaQQAFAdSPvmSdBPBmshyc1T4g+cjJ278CdDAjEEBBAPWjL9l9AmgPAbRLQd+Oer8CeNEEk61+FgRQOwigIID60ZesiwDaiX/+r0UTTLb6WRBA7SCAggDqR18yBJC5fwXoYEYggDIcAeRNpg+GABBAjSCAggDqRx8MASCAGkEABQHUjz5YFwG0XwX2bOUrwfhGsGrQdzAjEEBBAPWjD9ZJABvwfQDVoO9gRiCAggDqRx8MASCAGkEABQHUjz4YAkAANYIACgKoH30wvg8AAdQIAigIoH70wRAAAqgRBFAQQP3ogyEABFAjCKAggOoJMPMggBBlsCLryEEALQigcgLMPAggRBmsyDpyEEALAqicADMPAghRBiuyjhwE0IIAKifAzIMAQpTBiqwjBwG0IIDKCTDzIIAQZbAi68hBAC0IoHICzDwIIEQZrMg6cgIEQwB+ZE0WYOZBACHKYEXWkRMgGALwI2uyADMPAghRBiuyjpwAwRCAH1mTBZh5EECIMliRdeQECIYA/MiaLMDMgwBClMGKrCMnQDAE4EfWZAFmHgQQogxWZB05AYIhAD+yJgsw8yCAEGWwIuvICRAMAfiRNVmAmQcBhCiDFVlHToBgCMCPrMkCzDwIIEQZrMg6cgIEQwB2bARLlGydADMPAghRBiuyjpwAwRCAHQjADQQQogxWZB05AYIhADsQgBsIIEQZrMg6cgIEQwB2IAA3EECIMliRdeQECIYA7EAAbiCAEGWwIuvICRAMAdiBANxAACHKYEXWkRMgGAKwAwG4gQBClMGKrCMnQDAEYAcCcAMBhCiDFVlHToBgCMAOBOAGAhhXIUAdjMg6cgIEQwB2IAA3Bi+Aizn+rXAg68gJEAwB2IEA3Bi6AC4uUhsg68gJEAwB2IEA3Bi4AC4uchsg68gJEAwB2IEA3Bi2AC4ukhsg68gJEAwB2IEA3EAACKBG9MEQgB0IwI1BC+BiE/+WGJN15AQIhgDsQABuIAAEUCP6YAjADgTgBgJAADWiD4YA7EAAbiAABFAj+mAIwA4E4MagBZD+IHDCSDP0UwICsAMBuIEAEECN6KcEBGAHAnADASCAGtFPCQjADgTgxrAFkP1K4IyZpuinBARgBwJwY+ACSL4WUMpQE/RTAgKwAwG4MXQB5F4NNGeqFv2UgADsQABuDF4Aqb8PgGB2IAA7EIAbCCBEGYxImyxAMARgBwJwAwGEKIMRaZMFCIYA7BiIACLse0AAIWYTI9ImCxAMAdgxCAHEOPqIAELMJkYkShauZAjAjiEIIMj5hwggxGxiRKJk4UqGAOwYgACiXIGEAELMJkYkShauZAjAjvwCCLMGAQIIMZsYkShZuJIhADsQgBsIIMRsYkSiZOFKhgDMOD3NmmzOxSayliCAELOJEYmShSsZAjDidM7yriTJliCA1Sb4v2S42cSIRMnClQwB2HB6um2AHMlWQACrTfB/yXCziRGJkoUrGQIw4fR0hwFSJFsFAaw2wf8lw80mRiRKFq5kCMCC09NdBsiQbJ0o8z8CKCFmEyMSJQtXMgRgAQJwBgGEmE2MSJQsXMn0Atg8WSbBNHm6yfTuBMk2QAArTfB/yXCziQ3iiwx7JVzJ1AJIeah0MALgSuCVJvi/ZLjZxIII3as/wpVMLICch0qHIwDWAlo2wf8lw80mBgTpYH0RrmRaASTdUz4gAQR5g4YAQswmxEHWlgAAFSlJREFU/RPlI2ZfhCuZVABpp8mhHASeEGFoIoAQs0nvhDnI1BfhSoYALBiUACJ2YwSQAwRgjVIAu/eUZJgmEYAzCCBEGfrmYhN1gx5MuJIhABN2zf85ku0gYDdGAClAAOYgABt2zP9Jkm0TsBsjgBQgAHMQgBHb83+WZFsE7MYIIAUIwBwOApuxPvuXRMk2CNiNEUAOss3/8UqGAOzI/41gUwJ240EIYOMdRoAy9A4CsAYB2IEA3BigALb2MQYoQ+8gAGu4EtgOBODG8ASwPXAClKF/ks3/CGCD1CdLIgA3BieAHW+dApTBgFzzPwLYJPPJkgjAjaEJYNfO0wBlsCDV/I8Attk8WSbPNIkA3EAAIcpgQ5rZvyCAXaQ9WRIBuDEwAZxu0t4ZoAxGJEqGALZBAJUTsBsjgEwkSoYAtkEAlROwGyOATCRKhgC2QQCVE7AbI4BMJEqGALZBAJUTsBsnF8CwDgJnSoYAtkEAlROwGyOATCRKhgC2QQCVE7AbI4BMJEqGALZBAJUTsBtnF8BwrgRuSZQMAWyDAConYDdOL4ChrAU0IVEyBLANAqicgN04vwCGsRrolETJEMA2CKByAnbjAQhgEN8HMCVRso2K6ZMhADsQgBuDFEC8t5NGJEq2UrIYq9whADsQgBsIIEQZjEiUbHWPXQgDIAA7EIAbCCBEGYxIlGzlkH0MAyAAOxCAGwggRBmMSJRsec5uEAMgADsQgBsIIEQZjEiUDAFsgwAqJ8D4RAAhymBEomSLq7ajGAAB2JFXAOFmHgQQogxGJEqGALZBANURbuZBACHKYESiZAhgGwRQHeFmHgQQogxGJEqGALZBANURbuZBACVT/9okTzIOAm+TthsjADcQQMnUvzbJkwwBbJO2GyMANxBAydS/NsmTDAFsk7YbIwA3EEDJ1L82yZOMK4G3SduNEYAbCKBk6l+b5EnGWkDb0I2rAwHsaIL/SzJyqoPVQLehG1dHSgF8fNU0zfN3He6+HZ18t92EI17ygTByqoPvA9iGblwdGQVw00w4eXPv3XdvGwTgSp5k4UcO3diOPMnClezhArgdNU/elY+bc/uuu68aBOBLnmQIYBu6cXWEK9nDBXDZPPlQJu/un95z99gJCMCXPMkQwDZ04+oIV7IHC+DTi2a6k+em+eLHz949dsHvEYAveZIhgG3oxtURrmQPFsBigl9M+fvuvmqe3iAAX/IkQwDb0I2rI1zJHiyAq+munskb/PPP3X07+uJHBOBMnmQIYBu6cXWEK9mDBXC5MtM//czd43/fFATgTJ5kCGAbunF1hCtZDwJ4uvXTrruv2n/WBPCvc/7pzump/2vCg1gv2cWFqh0LtgTg3wS6cXWEK5mXAKaXgPEJwJk8yfgEsA3duDrClcxJALMjAQjAmTzJEMA2dOPqCFcyJwHMjgkjAGfyJEMA29CNqyNcyXwOAs/XAEIAzuRJhgC2oRtXR7iS+ZwGetWs8HRjC4wcO/IkQwDb0I2rI1zJfC4EQwAi8iRDANvQjasjXMkcl4Io7AJyJ08yBLAN3bg6wpXMczE4BOBOmmSnpxtfCCBryRwE4EeeZOFK5rocNALwJkmy0znzOxBAC924OsKVrIcvhHm/9s0vN81s38/77S+EQQDe5Eh2erppAATQQjeujnAl6/0rIRcC2PWVkAjAmRTJTk+3DIAAWujG1RGuZHwpfIlQBisyJDs93TYAAmihG1dHuJIhgBKhDFZkSIYA9kE3ro5wJUMAJUIZ+mIjWIZkp5u0dyKAlrTdeJM8ycKVDAGUCGXoCwTgBgLwI0+ycCVDACVCGfoCAbiBAPzIkyxcyRBAiVCGvkAAbiAAP/IkC1cyBFAilKEvEgqAg8B7SduNN8mTLFzJEECJUIa+QABuIAA/8iQLVzIEUCKUoS8QgBsIwI88ycKVDAGUCGXoi4wC4ErgfaTtxpvkSRauZAigRChDX6QUAGsB7SFtN94kT7JwJUMAJUIZ+iKnAFgNdDdpu/EmaZKt9uEWfTAEUCKUoS+SCoDvA9hJ2m68SZJkW+9jAgRDACVCGfoirQD4RrAdpO3Gm+RItr0nM0AwBFAilKEvEIAbCMCPFMl2nMsQIBgCKBHK0BcIwA0E4EeGZLvOZg4QDAGUCGXoCwTgBgLwI0MyBLC3Cf4vmXbkIAA3EIAfCZKdbjK5Vx8MAZQIZegLBOAGAvAjQTIEsL8J/i+ZduQgADcQgB8JkiGA/U3wf8m0IwcBuIEA/EiQDAHsb4L/S6YdOUMRQIBgCMCPDMk4CLy3Cf4vmXbkIAA3EIAfGZIhgL1N8H/JtCMHAbiBAPzIkAwB7G2C/0umHTkIwA0E4EeKZDvm/wDBEECJUIaeWOlaU9Iki1cyBOBHjmTb83+AYAigRChDL2x1rzTJSsCSIQA/kiTbHqD6YAigRChDH+x4g5EkWUu4kiEAP9Ik2/yIrg+GAEqEMvTArl2MOZJNCFcyBOBHnmThSoYASoQyPJydJxmkSDYlXMkQgB95koUrGQIoEcrwcBCAMwjAjzzJwpUMAZQIZXgwp5tM706QbEa4kiEAP/IkC1cyBFAilOHBIABvEIAfeZKFKxkCKBHK8GAQgDcIwI88ycKVDAGUCGV4MAjAGwTgR55k4UqGAEqEMjwcDgI7gwD8yJMsXMkQQIlQhoeDAJxBAH7kSRauZAigRCjDw0EAziAAP/IkC1cyBFAilKEHds3/OZJNCFcyBOBHnmThSoYASoQy9MGO+T9JspZwJUMAfuRJFq5kCKBEKEMvbM//WZKVgCVDAH7kSRauZAigRChDT6zP/iVRsnglQwB+5EkWrmQIoEQoQ1/wjWBuIAA/8iQLVzIEUCKUoS8QgBsIwI88ycKVDAGUCGXoCwTgBgLwI0+ycCVDACVCGfoCAbiBAPzIkyxcyRBAiVCGvkAAbiAAP/IkC1cyBFAilKEvEIAbCMCPPMnClQwBlAhl6AsE4AYC8CNPsnAlQwAlQhn6AgG4gQD8yJMsXMkQQIlQhr5AAG4gAD/yJAtXMgRQIpShLxCAGwjAjzzJwpUMAZQIZegLBOAGAvAjT7JwJUMAJUIZ+gIBuIEA/MiTLFzJEECJUIa+QABuIAA/8iQLVzIEUCKUoS8QgBsIwI88ycKVDAGUCGXoCwTgBgLwI0+ycCVDACVCGfoCAbiBAPzIkyxcyRBAiVCGvkAAbiAAP/IkC1cyBFAilKEvEIAbCMCPPMnClQwBlAhl6AsE4AYC8CNPsnAlQwAlQhn6AgG4gQD8yJMsXMkQQIlQhr5AAG4gAD/yJAtXMgRQIpTBijzJwpUMAfiRJ1m4kiGAEqEMVuRJFq5kCMCPPMnClQwBlAhlsCJPsnAlQwB+5EkWrmQIoEQogxV5koUrGQLwI0+ycCVDACVCGazIkyxcyRCAH3mShSsZAigRymBFnmThSoYA/EibTB8MAZQIZbAiT7JwJUMAfqRNpg+GAEqEMliRJ1m4kiEAP9Im0wdDACVCGazIkyxcyRCAH2mT6YMhgBKhDFbkSRauZAjAj7TJ9MEQQIlQBivyJAtXMgTgR9pk+mAIoEQogxV5koUrGQLwI20yfTAEUCKUwYo8ycKVDAH4kTaZPhgCKBHKYEWeZOFKhgD8SJtMHwwBlAhlsCJPsnAlQwB+pE2mD4YASoQyWJEnWbiSIQA/0ibTB0MAJUIZrMiTLFzJEIAfaZPpgyGAEqEMVuRJFq5kCMCPtMn0wRBAiVAGK/IkC1cyBOBH2mT6YAigRCiDFWmT6YMhAD/SJtMHQwAlQhmsSJtMHwwB+JE2mT4YAigRymBF2mT6YAjAj7TJ9MEQQIlQBivSJtMHQwB+pE2mD4YASoQyWJE2mT4YAvAjbTJ9MARQIpTBirTJ9MEQgB9pk+mDIYASoQxWpE2mD4YA/EibTB8MAZQIZbAibTJ9MATgR9pk+mAIoEQogxVpk+mDIQA/0ibTB0MAJUIZrEibTB8sgAA20P9NrEibTB9smALYQF8GK9Im0wdDAH6kTaYPhgBKhDJYkTaZPhgC8CNtMn0wBFAilMGKtMn0wRCAH2mT6YMhgBKhDFakTaYPhgD8SJtMHwwBlAhlsCJtMn0wBOBH2mT6YAigRCiDFWmT6YMhAD/SJtMHQwAlQhmsSJtMHwwB+JE2mT4YAigRymBF2mT6YAjAj7TJ9MEQQIlQBivSJtMHQwB+pE2mD4YASoQyWJE2mT4YAvAjbTJ9MARQIpTBirTJ9MEQgB9pk+mDIYASoQxWpE2mD4YA/EibTB8MAZQIZbAibTJ9MATgR9pk+mAIoEQogxVpk+mDIQA/0ibTB0MAJUIZrEibTB8MAfiRNpk+GAIoEcpgRdpk+mAIwI+0yfTBEECJUAYr0ibTB0MAfqRNpg+GAEqEMliRNpk+GALwI20yfTAEUCKUwYq0yfTBEIAfaZPpgyGAEqEMVqRNpg+GAPxIm0wfDAGUCGWwIm0yfTAE4EfaZPpgCKBEKIMVaZPpgyEAP9Im0wdDACVCGaxIm0wfDAH4kTaZPhgCKBHKYEXaZPpgCMCPtMn0wRBAiVAGK9Im0wdDAH6kTaYPhgBKhDJYkTaZPhgC8CNtMn0wBFAilMGKtMn0wRCAH2mT6YMhgBKhDFakTaYPhgD8SJtMHwwBlAhlsCJtMn0wBOBH2mT6YAigRCiDFWmT6YMhAD/SJtMHQwAlQhmsSJtMHwwB+JE2mT4YAigRymBF2mT6YAjAj7TJ9MEQQIlQBivSJtMHQwB+pE2mD4YASoQyWJE2mT4YAvAjbTJ9sAgCAKgRujEkYL0bIwCAbtCNIQHr3VggAD36T+9WpE2WNtgDyPs3SZssXjAEkIq0ydIGewB5/yZpk8ULhgBSkTZZ2mAPIO/fJG2yeMEQQCrSJksb7AHk/ZukTRYvGAJIRdpkaYM9gLx/k7TJ4gVDAKlImyxtsAeQ92+SNlm8YIMUAAAAIAAAgMGCAAAABgoCAAAYKAgAAGCgIAAAgIGCAAAABgoCAAAYKCkF8OufnzVN8/zd7pv7nlZ+etk0J199cGvmMbx/1rbxl/nNj692Jisfvxk1zaPXsyxXzYynbu3syqcXzfmDN1JN2sOgG1dT2Hq7cUYB3Ixmf7rzXTf3Pe3ucnrr5DvXxh7E3dv1Nt7Mbr7ZeN77WbQn0850mXvk1JP2IOjG9RS23m6cUAC3o+bxD2OfjnvZm+2b+57W/sGfvCt330ceOpfNyetxk182X/zY3hxHGDd5nGCjyeMB9Xgc5U+zzvPpRdxIPYycitIeAt24osLW240TCuBq1q/G7zNamW7c3Pe0cS98Ors/3huMGeM2Tgb5vLtdThONE6w1eZF0FvF29CTsDoGHj5ya0h4C3biiwtbbjfMJYNmPbtq3FBs39z1t/HefPRr4jcZ8sI+HTNv2ca+bvhm8md9fZjdnCWYj7SbuXNDDyKkp7QHQjWsqbL3dOJ8AltyO1sbAxs2N+y/nb6zGQ2pzX2Q4piNnMWAWQ2j+6Pqbh6tYgX7981m7w/fLyTG/duR8/Gb1qOVPkwOC37Y/rtZkMhx+Xj62IHjah0M3nhKssEm6cWYBrL9X2ry5cf/lwriXPRzQt2X2JuFqpWOdLx9dvzXm8ov/uz2/4PkPbg38HIuDlpMM45Hzm9HKEcG772cPtiNp813R/LHVoRI8bQ/QjafEKmyWbpxZAJfrHyk3bm7cvxg5m7si4/HTaNp3tt5ZTGnfSN2159k9ej2/vfv8EQnj1jwZv2m6ez+a/NnbxrVHBN8vMrVjph0/T1c+WE93Glw1zW/Hj/1tVE/aPqAbz28HKmyabpxYALdrf+HNm5v3X618EI08cian0P12MmJW3+2tNHkc6PXblfcY49snX38ov34fYugs/s7TvdVtR5981h238nz+38Wja4cH21yTx9beA8dO2wN044iFTdON8wpg/Odefau0cXPr/pWahR45n/7D2fiT4ZfTtxn7Rs7q+4/2/LI3s2ftfu+o4WY+chbnPY9/WJ2/zhdjZPrRebGrYC1uJWmPhm4cu7C1d+O0ArhbP2H6bvP86a37l5/aQo+cltmH58+MnPPJT1fru4t7uVyxF+5+/v9+18xHzvn0vnbQrOwEmAyk2aOT8bSyl/RqZfdpBWkfAt04cGEzdOOsAmhHxPnem7vun1+eeH4ZfuTM3h/sHzmzjrXReYLsFp6cHzE/XrY886N9m7Q1OibjZ9ru5V7RMct3RdHTPgy6cYla2CTdOKkA9g6c29Vj9etPu/tze9T93dbx+IBMe8X60bNFsuVe4s3OE2JOmFzifvKbb9/vGDkrnX/6KXpyiuD0o/P+kRM57cOgGy+ftiREYbN045wC+PSymR1p2by5NnI2njZ7cphPmJ9h0ivWz59bJFsJcBlv5Fw1zZeTU9tuurx1mjx8NT/R4nzH9mKnfRh045WnbfyWmDTdOKUAPr5oVheW2rh5z/37LrSJxPRdwp4raJZvITbeTER4U7hs0lWHnaeT7j+7c89n4dBpHwbdePOn+c1ztybuIU83ziiAcTVWO//GzT33L07Kugp1ksEal+v7CfddQ79xDsLit+ZrsChZjJS7t/ORM+vus3eDa02fRPv/R/NlznYWJnLaB0E3jlvYPN04oQDu1pcVvNtaZXDn/fPz5xbn0QVk5dywSdP3rKK12J94OdsBObs6cWMZMQmLtt4sjp5NWzfNtnEC9eTx3y0XjXy6vo2ydnfAtA+Bbhy4sHm6cUIBXK2fKXe1+8S5rfuna9T+NArQvfbRDvbXH8qv3zSLUb5zHd12B+Uvk1MBz+e/NVl9V//OaXLw7Lez0xTnx8T2XkI5e/7Kj+3KKz+/XM8bOe0DoBtHLmyabpxPAGuH2c83b+572vRQWsvjqJ+cy7KN8yDvZyeibfaRxUokk1sfZ791snWkUMD87/74v45mn///OFr5u68totJy0yyGwOKxjbyB0x4P3Th0YdN043wCuF2s0jTpYRs39z2ttOfPtUtxfB32jVPLZLmQ2QqELfu+S299vcFJspXfknL3/WjyZ16cFv1msozi4u8+XUZxsQbW+AnLfaaTxx4tv0pwRuC0R0M3LqELm6Ub5xMAAAB0AgEAAAwUBAAAMFAQAADAQEEAAAADBQEAAAwUBAAAMFAQAADAQEEAAAADBQEAAAwUBAAAMFAQAADAQEEAAAADBQEAAAwUBAAAMFAQAADAQEEAAAADBQEAAAwUBAAAMFD+N2OZ/xv7W5OtAAAAAElFTkSuQmCC" class="img-fluid figure-img" width="768">
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-fig_d4-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure 10: Predicted probabilities of voting for a green party by education, ESS wave and age group (Appendix)
</figcaption>
</figure>
</div>
</div>
</div>
</section>
<section id="marginal-effects-of-women-vs.-men-on-voting-for-the-greens" class="level2" data-number="3.8">
<h2 data-number="3.8" class="anchored" data-anchor-id="marginal-effects-of-women-vs.-men-on-voting-for-the-greens"><span class="header-section-number">3.8</span> Marginal effects of women vs. men on voting for the greens</h2>
<div class="cell">
<details class="code-fold">
<summary>Show the code</summary>
<div class="sourceCode cell-code" id="cb14"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a><span class="co"># contrasting women and men</span></span>
<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a>me_three_green_gender <span class="ot">&lt;-</span> <span class="fu">avg_slopes</span>(</span>
<span id="cb14-3"><a href="#cb14-3" aria-hidden="true" tabindex="-1"></a>  three_green,</span>
<span id="cb14-4"><a href="#cb14-4" aria-hidden="true" tabindex="-1"></a>  <span class="at">variables =</span> <span class="st">&quot;gndr_fct&quot;</span>,</span>
<span id="cb14-5"><a href="#cb14-5" aria-hidden="true" tabindex="-1"></a>  <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&quot;cohort_fct&quot;</span>, <span class="st">&quot;edu_fct&quot;</span>),</span>
<span id="cb14-6"><a href="#cb14-6" aria-hidden="true" tabindex="-1"></a>  <span class="at">newdata =</span> <span class="fu">datagrid</span>(</span>
<span id="cb14-7"><a href="#cb14-7" aria-hidden="true" tabindex="-1"></a>    <span class="at">gndr_fct =</span> <span class="fu">c</span>(<span class="st">&quot;men&quot;</span>, <span class="st">&quot;women&quot;</span>),</span>
<span id="cb14-8"><a href="#cb14-8" aria-hidden="true" tabindex="-1"></a>    <span class="at">cohort_fct =</span> <span class="fu">c</span>(</span>
<span id="cb14-9"><a href="#cb14-9" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;interwar&quot;</span>,</span>
<span id="cb14-10"><a href="#cb14-10" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;post-war&quot;</span>,</span>
<span id="cb14-11"><a href="#cb14-11" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;60-70s&quot;</span>,</span>
<span id="cb14-12"><a href="#cb14-12" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;80s&quot;</span>,</span>
<span id="cb14-13"><a href="#cb14-13" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;90s&quot;</span>,</span>
<span id="cb14-14"><a href="#cb14-14" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;millennial&quot;</span></span>
<span id="cb14-15"><a href="#cb14-15" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb14-16"><a href="#cb14-16" aria-hidden="true" tabindex="-1"></a>    <span class="at">edu_fct =</span> <span class="fu">c</span>(</span>
<span id="cb14-17"><a href="#cb14-17" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;(below) lower secondary&quot;</span>,</span>
<span id="cb14-18"><a href="#cb14-18" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;upper secondary&quot;</span>,</span>
<span id="cb14-19"><a href="#cb14-19" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;tertiary&quot;</span></span>
<span id="cb14-20"><a href="#cb14-20" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb14-21"><a href="#cb14-21" aria-hidden="true" tabindex="-1"></a>    <span class="at">grid_type =</span> <span class="st">&quot;counterfactual&quot;</span></span>
<span id="cb14-22"><a href="#cb14-22" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb14-23"><a href="#cb14-23" aria-hidden="true" tabindex="-1"></a>    <span class="at">re.form =</span> <span class="cn">NA</span></span>
<span id="cb14-24"><a href="#cb14-24" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb14-25"><a href="#cb14-25" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb14-26"><a href="#cb14-26" aria-hidden="true" tabindex="-1"></a>me_three_green_gender <span class="ot">&lt;-</span> me_three_green_gender <span class="sc">%&gt;%</span></span>
<span id="cb14-27"><a href="#cb14-27" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">contrast =</span> <span class="fu">case_when</span>(</span>
<span id="cb14-28"><a href="#cb14-28" aria-hidden="true" tabindex="-1"></a>    contrast <span class="sc">==</span> <span class="st">&quot;women - men&quot;</span> <span class="sc">~</span></span>
<span id="cb14-29"><a href="#cb14-29" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;women vs men&quot;</span></span>
<span id="cb14-30"><a href="#cb14-30" aria-hidden="true" tabindex="-1"></a>  ))</span>
<span id="cb14-31"><a href="#cb14-31" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb14-32"><a href="#cb14-32" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> me_three_green_gender, <span class="fu">aes</span>(</span>
<span id="cb14-33"><a href="#cb14-33" aria-hidden="true" tabindex="-1"></a>  <span class="at">x =</span> cohort_fct, <span class="at">y =</span> estimate,</span>
<span id="cb14-34"><a href="#cb14-34" aria-hidden="true" tabindex="-1"></a>  <span class="at">ymin =</span> conf.low, <span class="at">ymax =</span> conf.high,</span>
<span id="cb14-35"><a href="#cb14-35" aria-hidden="true" tabindex="-1"></a>  <span class="at">color =</span> edu_fct</span>
<span id="cb14-36"><a href="#cb14-36" aria-hidden="true" tabindex="-1"></a>)) <span class="sc">+</span></span>
<span id="cb14-37"><a href="#cb14-37" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept =</span> <span class="dv">0</span>, <span class="at">color =</span> <span class="st">&quot;grey70&quot;</span>) <span class="sc">+</span></span>
<span id="cb14-38"><a href="#cb14-38" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_pointrange</span>(<span class="at">position =</span> <span class="fu">position_dodge</span>(<span class="at">width =</span> <span class="fu">c</span>(<span class="fl">0.2</span>))) <span class="sc">+</span></span>
<span id="cb14-39"><a href="#cb14-39" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb14-40"><a href="#cb14-40" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> <span class="cn">NULL</span>, <span class="at">color =</span> <span class="st">&quot;Education&quot;</span>,</span>
<span id="cb14-41"><a href="#cb14-41" aria-hidden="true" tabindex="-1"></a>    <span class="at">y =</span> <span class="st">&quot;Marginal effect of women vs. men&quot;</span></span>
<span id="cb14-42"><a href="#cb14-42" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb14-43"><a href="#cb14-43" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_viridis_d</span>(<span class="at">option =</span> <span class="st">&quot;plasma&quot;</span>, <span class="at">end =</span> .<span class="dv">8</span>) <span class="sc">+</span></span>
<span id="cb14-44"><a href="#cb14-44" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_light</span>() <span class="sc">+</span></span>
<span id="cb14-45"><a href="#cb14-45" aria-hidden="true" tabindex="-1"></a>  <span class="fu">my_theme</span>() <span class="sc">+</span></span>
<span id="cb14-46"><a href="#cb14-46" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;top&quot;</span>)</span>
<span id="cb14-47"><a href="#cb14-47" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb14-48"><a href="#cb14-48" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(</span>
<span id="cb14-49"><a href="#cb14-49" aria-hidden="true" tabindex="-1"></a>  <span class="at">filename =</span> <span class="fu">here</span>(<span class="st">&quot;figs&quot;</span>, <span class="st">&quot;final&quot;</span>, <span class="st">&quot;appendix_fig_d5.png&quot;</span>),</span>
<span id="cb14-50"><a href="#cb14-50" aria-hidden="true" tabindex="-1"></a>  <span class="at">units =</span> <span class="fu">c</span>(<span class="st">&quot;in&quot;</span>),</span>
<span id="cb14-51"><a href="#cb14-51" aria-hidden="true" tabindex="-1"></a>  <span class="at">width =</span> <span class="dv">6</span>, <span class="at">height =</span> <span class="dv">5</span>,</span>
<span id="cb14-52"><a href="#cb14-52" aria-hidden="true" tabindex="-1"></a>  <span class="at">dpi =</span> <span class="dv">300</span></span>
<span id="cb14-53"><a href="#cb14-53" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<div id="fig-fig_d5" class="quarto-float quarto-figure quarto-figure-center anchored">
<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-fig_d5-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
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" class="img-fluid figure-img" width="768">
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-fig_d5-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure 11: Marginal effects of women vs. men on voting for the greens across cohorts and education levels (Appendix)
</figcaption>
</figure>
</div>
</div>
</div>
</section>
<section id="remove-files" class="level2" data-number="3.9">
<h2 data-number="3.9" class="anchored" data-anchor-id="remove-files"><span class="header-section-number">3.9</span> Remove files</h2>
<div class="cell">
<details class="code-fold">
<summary>Show the code</summary>
<div class="sourceCode cell-code" id="cb15"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a><span class="fu">rm</span>(<span class="at">list =</span> <span class="fu">c</span>(<span class="st">&quot;df&quot;</span>, <span class="st">&quot;three_green&quot;</span>, <span class="st">&quot;two_green&quot;</span>, <span class="st">&quot;me_three_green_edu&quot;</span>, <span class="st">&quot;me_three_green_gender&quot;</span>,</span>
<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>            <span class="st">&quot;me_two_green&quot;</span>, <span class="st">&quot;pp_two_green&quot;</span>, <span class="st">&quot;pp_two_green_age&quot;</span>, <span class="st">&quot;pp_two_green_period&quot;</span>,</span>
<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a>               <span class="st">&quot;pp_three_green&quot;</span>, <span class="st">&quot;pp_three_green_age&quot;</span>, <span class="st">&quot;pp_three_green_period&quot;</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
</div>
</section>
</section>
<section id="radical-right-vote" class="level1" data-number="4">
<h1 data-number="4"><span class="header-section-number">4</span> Radical right vote</h1>
<section id="multilevel-model-voting-for-the-radical-right-two-way-interaction" class="level2" data-number="4.1">
<h2 data-number="4.1" class="anchored" data-anchor-id="multilevel-model-voting-for-the-radical-right-two-way-interaction"><span class="header-section-number">4.1</span> Multilevel model: Voting for the radical right (two-way interaction)</h2>
<div class="cell">
<details class="code-fold">
<summary>Show the code</summary>
<div class="sourceCode cell-code" id="cb16"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a><span class="co"># model</span></span>
<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a>two_rr <span class="ot">&lt;-</span> <span class="fu">glmer</span>(</span>
<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a>  vote_radright <span class="sc">~</span></span>
<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a>    cohort_fct <span class="sc">*</span> edu_fct <span class="sc">+</span></span>
<span id="cb16-5"><a href="#cb16-5" aria-hidden="true" tabindex="-1"></a>    age_grp_fct <span class="sc">*</span> edu_fct <span class="sc">+</span></span>
<span id="cb16-6"><a href="#cb16-6" aria-hidden="true" tabindex="-1"></a>    <span class="fu">factor</span>(essround) <span class="sc">*</span> edu_fct <span class="sc">+</span></span>
<span id="cb16-7"><a href="#cb16-7" aria-hidden="true" tabindex="-1"></a>    gndr_fct <span class="sc">+</span> immigrant <span class="sc">+</span> <span class="fu">factor</span>(cntry_id) <span class="sc">+</span></span>
<span id="cb16-8"><a href="#cb16-8" aria-hidden="true" tabindex="-1"></a>    (<span class="dv">1</span> <span class="sc">|</span> cntry_wave_id),</span>
<span id="cb16-9"><a href="#cb16-9" aria-hidden="true" tabindex="-1"></a>  <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">&quot;logit&quot;</span>),</span>
<span id="cb16-10"><a href="#cb16-10" aria-hidden="true" tabindex="-1"></a>  <span class="at">nAGQ =</span> <span class="dv">0</span>,</span>
<span id="cb16-11"><a href="#cb16-11" aria-hidden="true" tabindex="-1"></a>  <span class="at">data =</span> dfradicalright</span>
<span id="cb16-12"><a href="#cb16-12" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb16-13"><a href="#cb16-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb16-14"><a href="#cb16-14" aria-hidden="true" tabindex="-1"></a><span class="co"># predicted probabilities</span></span>
<span id="cb16-15"><a href="#cb16-15" aria-hidden="true" tabindex="-1"></a>pp_two_rr <span class="ot">&lt;-</span> <span class="fu">predictions</span>(two_rr,</span>
<span id="cb16-16"><a href="#cb16-16" aria-hidden="true" tabindex="-1"></a>  <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&quot;cohort_fct&quot;</span>, <span class="st">&quot;edu_fct&quot;</span>),</span>
<span id="cb16-17"><a href="#cb16-17" aria-hidden="true" tabindex="-1"></a>  <span class="at">newdata =</span> <span class="fu">datagrid</span>(</span>
<span id="cb16-18"><a href="#cb16-18" aria-hidden="true" tabindex="-1"></a>    <span class="at">cohort_fct =</span> <span class="fu">c</span>(</span>
<span id="cb16-19"><a href="#cb16-19" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;interwar&quot;</span>,</span>
<span id="cb16-20"><a href="#cb16-20" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;post-war&quot;</span>,</span>
<span id="cb16-21"><a href="#cb16-21" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;60-70s&quot;</span>,</span>
<span id="cb16-22"><a href="#cb16-22" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;80s&quot;</span>,</span>
<span id="cb16-23"><a href="#cb16-23" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;90s&quot;</span>,</span>
<span id="cb16-24"><a href="#cb16-24" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;millennial&quot;</span></span>
<span id="cb16-25"><a href="#cb16-25" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb16-26"><a href="#cb16-26" aria-hidden="true" tabindex="-1"></a>    <span class="at">edu_fct =</span> <span class="fu">c</span>(</span>
<span id="cb16-27"><a href="#cb16-27" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;(below) lower secondary&quot;</span>,</span>
<span id="cb16-28"><a href="#cb16-28" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;upper secondary&quot;</span>,</span>
<span id="cb16-29"><a href="#cb16-29" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;tertiary&quot;</span></span>
<span id="cb16-30"><a href="#cb16-30" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb16-31"><a href="#cb16-31" aria-hidden="true" tabindex="-1"></a>    <span class="at">grid_type =</span> <span class="st">&quot;counterfactual&quot;</span></span>
<span id="cb16-32"><a href="#cb16-32" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb16-33"><a href="#cb16-33" aria-hidden="true" tabindex="-1"></a>    <span class="at">re.form =</span> <span class="cn">NA</span></span>
<span id="cb16-34"><a href="#cb16-34" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb16-35"><a href="#cb16-35" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb16-36"><a href="#cb16-36" aria-hidden="true" tabindex="-1"></a>plot_pp_two_rr <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(</span>
<span id="cb16-37"><a href="#cb16-37" aria-hidden="true" tabindex="-1"></a>  <span class="at">data =</span> pp_two_rr,</span>
<span id="cb16-38"><a href="#cb16-38" aria-hidden="true" tabindex="-1"></a>  <span class="fu">aes</span>(</span>
<span id="cb16-39"><a href="#cb16-39" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> cohort_fct, <span class="at">y =</span> estimate, <span class="at">ymin =</span> conf.low,</span>
<span id="cb16-40"><a href="#cb16-40" aria-hidden="true" tabindex="-1"></a>    <span class="at">ymax =</span> conf.high, <span class="at">color =</span> edu_fct</span>
<span id="cb16-41"><a href="#cb16-41" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb16-42"><a href="#cb16-42" aria-hidden="true" tabindex="-1"></a>) <span class="sc">+</span></span>
<span id="cb16-43"><a href="#cb16-43" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept =</span> <span class="dv">0</span>, <span class="at">color =</span> <span class="st">&quot;grey70&quot;</span>) <span class="sc">+</span></span>
<span id="cb16-44"><a href="#cb16-44" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_pointrange</span>(<span class="at">position =</span> <span class="fu">position_dodge</span>(<span class="at">width =</span> <span class="fu">c</span>(<span class="fl">0.2</span>))) <span class="sc">+</span></span>
<span id="cb16-45"><a href="#cb16-45" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb16-46"><a href="#cb16-46" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> <span class="cn">NULL</span>, <span class="at">y =</span> <span class="st">&quot;Predicted probability&quot;</span>,</span>
<span id="cb16-47"><a href="#cb16-47" aria-hidden="true" tabindex="-1"></a>    <span class="at">color =</span> <span class="cn">NULL</span></span>
<span id="cb16-48"><a href="#cb16-48" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb16-49"><a href="#cb16-49" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_viridis_d</span>(<span class="at">option =</span> <span class="st">&quot;plasma&quot;</span>, <span class="at">end =</span> .<span class="dv">8</span>) <span class="sc">+</span></span>
<span id="cb16-50"><a href="#cb16-50" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_light</span>() <span class="sc">+</span></span>
<span id="cb16-51"><a href="#cb16-51" aria-hidden="true" tabindex="-1"></a>  <span class="fu">my_theme</span>() <span class="sc">+</span></span>
<span id="cb16-52"><a href="#cb16-52" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;top&quot;</span>)</span>
<span id="cb16-53"><a href="#cb16-53" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb16-54"><a href="#cb16-54" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(</span>
<span id="cb16-55"><a href="#cb16-55" aria-hidden="true" tabindex="-1"></a>  <span class="at">filename =</span> <span class="fu">here</span>(<span class="st">&quot;figs&quot;</span>, <span class="st">&quot;final&quot;</span>, <span class="st">&quot;fig_3_upper.png&quot;</span>),</span>
<span id="cb16-56"><a href="#cb16-56" aria-hidden="true" tabindex="-1"></a>  <span class="at">units =</span> <span class="fu">c</span>(<span class="st">&quot;in&quot;</span>),</span>
<span id="cb16-57"><a href="#cb16-57" aria-hidden="true" tabindex="-1"></a>  <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">5</span>,</span>
<span id="cb16-58"><a href="#cb16-58" aria-hidden="true" tabindex="-1"></a>  <span class="at">dpi =</span> <span class="dv">300</span></span>
<span id="cb16-59"><a href="#cb16-59" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb16-60"><a href="#cb16-60" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb16-61"><a href="#cb16-61" aria-hidden="true" tabindex="-1"></a><span class="co"># marginal effects</span></span>
<span id="cb16-62"><a href="#cb16-62" aria-hidden="true" tabindex="-1"></a>me_two_rr <span class="ot">&lt;-</span> <span class="fu">avg_slopes</span>(</span>
<span id="cb16-63"><a href="#cb16-63" aria-hidden="true" tabindex="-1"></a>  two_rr,</span>
<span id="cb16-64"><a href="#cb16-64" aria-hidden="true" tabindex="-1"></a>  <span class="at">variables =</span> <span class="st">&quot;edu_fct&quot;</span>,</span>
<span id="cb16-65"><a href="#cb16-65" aria-hidden="true" tabindex="-1"></a>  <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&quot;cohort_fct&quot;</span>),</span>
<span id="cb16-66"><a href="#cb16-66" aria-hidden="true" tabindex="-1"></a>  <span class="at">newdata =</span> <span class="fu">datagrid</span>(</span>
<span id="cb16-67"><a href="#cb16-67" aria-hidden="true" tabindex="-1"></a>    <span class="at">cohort_fct =</span> <span class="fu">c</span>(</span>
<span id="cb16-68"><a href="#cb16-68" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;interwar&quot;</span>,</span>
<span id="cb16-69"><a href="#cb16-69" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;post-war&quot;</span>,</span>
<span id="cb16-70"><a href="#cb16-70" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;60-70s&quot;</span>,</span>
<span id="cb16-71"><a href="#cb16-71" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;80s&quot;</span>,</span>
<span id="cb16-72"><a href="#cb16-72" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;90s&quot;</span>,</span>
<span id="cb16-73"><a href="#cb16-73" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;millennial&quot;</span></span>
<span id="cb16-74"><a href="#cb16-74" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb16-75"><a href="#cb16-75" aria-hidden="true" tabindex="-1"></a>    <span class="at">edu_fct =</span> <span class="fu">c</span>(</span>
<span id="cb16-76"><a href="#cb16-76" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;(below) lower secondary&quot;</span>,</span>
<span id="cb16-77"><a href="#cb16-77" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;upper secondary&quot;</span>,</span>
<span id="cb16-78"><a href="#cb16-78" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;tertiary&quot;</span></span>
<span id="cb16-79"><a href="#cb16-79" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb16-80"><a href="#cb16-80" aria-hidden="true" tabindex="-1"></a>    <span class="at">grid_type =</span> <span class="st">&quot;counterfactual&quot;</span></span>
<span id="cb16-81"><a href="#cb16-81" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb16-82"><a href="#cb16-82" aria-hidden="true" tabindex="-1"></a>    <span class="at">re.form =</span> <span class="cn">NA</span></span>
<span id="cb16-83"><a href="#cb16-83" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb16-84"><a href="#cb16-84" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb16-85"><a href="#cb16-85" aria-hidden="true" tabindex="-1"></a>me_two_rr <span class="ot">&lt;-</span> me_two_rr <span class="sc">%&gt;%</span></span>
<span id="cb16-86"><a href="#cb16-86" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">contrast =</span> <span class="fu">case_when</span>(</span>
<span id="cb16-87"><a href="#cb16-87" aria-hidden="true" tabindex="-1"></a>    contrast <span class="sc">==</span> <span class="st">&quot;tertiary - (below) lower secondary&quot;</span> <span class="sc">~</span></span>
<span id="cb16-88"><a href="#cb16-88" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;high vs low edu&quot;</span>,</span>
<span id="cb16-89"><a href="#cb16-89" aria-hidden="true" tabindex="-1"></a>    contrast <span class="sc">==</span> <span class="st">&quot;upper secondary - (below) lower secondary&quot;</span> <span class="sc">~</span></span>
<span id="cb16-90"><a href="#cb16-90" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;medium vs low edu&quot;</span></span>
<span id="cb16-91"><a href="#cb16-91" aria-hidden="true" tabindex="-1"></a>  ))</span>
<span id="cb16-92"><a href="#cb16-92" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb16-93"><a href="#cb16-93" aria-hidden="true" tabindex="-1"></a>plot_me_two_rr <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(<span class="at">data =</span> me_two_rr, <span class="fu">aes</span>(</span>
<span id="cb16-94"><a href="#cb16-94" aria-hidden="true" tabindex="-1"></a>  <span class="at">x =</span> cohort_fct, <span class="at">y =</span> estimate,</span>
<span id="cb16-95"><a href="#cb16-95" aria-hidden="true" tabindex="-1"></a>  <span class="at">ymin =</span> conf.low, <span class="at">ymax =</span> conf.high,</span>
<span id="cb16-96"><a href="#cb16-96" aria-hidden="true" tabindex="-1"></a>  <span class="at">color =</span> contrast</span>
<span id="cb16-97"><a href="#cb16-97" aria-hidden="true" tabindex="-1"></a>)) <span class="sc">+</span></span>
<span id="cb16-98"><a href="#cb16-98" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept =</span> <span class="dv">0</span>, <span class="at">color =</span> <span class="st">&quot;grey70&quot;</span>) <span class="sc">+</span></span>
<span id="cb16-99"><a href="#cb16-99" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_pointrange</span>(<span class="at">position =</span> <span class="fu">position_dodge</span>(<span class="at">width =</span> <span class="fu">c</span>(<span class="fl">0.2</span>))) <span class="sc">+</span></span>
<span id="cb16-100"><a href="#cb16-100" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb16-101"><a href="#cb16-101" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> <span class="cn">NULL</span>, <span class="at">color =</span> <span class="st">&quot;Education&quot;</span>,</span>
<span id="cb16-102"><a href="#cb16-102" aria-hidden="true" tabindex="-1"></a>    <span class="at">y =</span> <span class="st">&quot;Marginal effect of education&quot;</span></span>
<span id="cb16-103"><a href="#cb16-103" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb16-104"><a href="#cb16-104" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;#fe9f6d&quot;</span>, <span class="st">&quot;#8c2981&quot;</span>)) <span class="sc">+</span></span>
<span id="cb16-105"><a href="#cb16-105" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_light</span>() <span class="sc">+</span></span>
<span id="cb16-106"><a href="#cb16-106" aria-hidden="true" tabindex="-1"></a>  <span class="fu">my_theme</span>() <span class="sc">+</span></span>
<span id="cb16-107"><a href="#cb16-107" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;top&quot;</span>)</span>
<span id="cb16-108"><a href="#cb16-108" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb16-109"><a href="#cb16-109" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(</span>
<span id="cb16-110"><a href="#cb16-110" aria-hidden="true" tabindex="-1"></a>  <span class="at">filename =</span> <span class="fu">here</span>(<span class="st">&quot;figs&quot;</span>, <span class="st">&quot;final&quot;</span>, <span class="st">&quot;fig_3_lower.png&quot;</span>),</span>
<span id="cb16-111"><a href="#cb16-111" aria-hidden="true" tabindex="-1"></a>  <span class="at">units =</span> <span class="fu">c</span>(<span class="st">&quot;in&quot;</span>),</span>
<span id="cb16-112"><a href="#cb16-112" aria-hidden="true" tabindex="-1"></a>  <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">5</span>,</span>
<span id="cb16-113"><a href="#cb16-113" aria-hidden="true" tabindex="-1"></a>  <span class="at">dpi =</span> <span class="dv">300</span></span>
<span id="cb16-114"><a href="#cb16-114" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb16-115"><a href="#cb16-115" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb16-116"><a href="#cb16-116" aria-hidden="true" tabindex="-1"></a><span class="co"># Combine the plots vertically</span></span>
<span id="cb16-117"><a href="#cb16-117" aria-hidden="true" tabindex="-1"></a>combined_plot <span class="ot">&lt;-</span> plot_pp_two_rr <span class="sc">/</span> plot_me_two_rr</span>
<span id="cb16-118"><a href="#cb16-118" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb16-119"><a href="#cb16-119" aria-hidden="true" tabindex="-1"></a><span class="co"># Save the combined plot</span></span>
<span id="cb16-120"><a href="#cb16-120" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(</span>
<span id="cb16-121"><a href="#cb16-121" aria-hidden="true" tabindex="-1"></a>  <span class="at">filename =</span> <span class="fu">here</span>(<span class="st">&quot;figs&quot;</span>, <span class="st">&quot;final&quot;</span>, <span class="st">&quot;figure_3.png&quot;</span>),</span>
<span id="cb16-122"><a href="#cb16-122" aria-hidden="true" tabindex="-1"></a>  <span class="at">plot =</span> combined_plot,</span>
<span id="cb16-123"><a href="#cb16-123" aria-hidden="true" tabindex="-1"></a>  <span class="at">units =</span> <span class="fu">c</span>(<span class="st">&quot;in&quot;</span>),</span>
<span id="cb16-124"><a href="#cb16-124" aria-hidden="true" tabindex="-1"></a>  <span class="at">width =</span> <span class="dv">8</span>,</span>
<span id="cb16-125"><a href="#cb16-125" aria-hidden="true" tabindex="-1"></a>  <span class="at">height =</span> <span class="dv">10</span>,</span>
<span id="cb16-126"><a href="#cb16-126" aria-hidden="true" tabindex="-1"></a>  <span class="at">dpi =</span> <span class="dv">300</span></span>
<span id="cb16-127"><a href="#cb16-127" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb16-128"><a href="#cb16-128" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb16-129"><a href="#cb16-129" aria-hidden="true" tabindex="-1"></a>combined_plot</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<div aria-describedby="fig-fig_3-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<img role="img" 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" class="img-fluid figure-img" width="768">
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-fig_3-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure 12: Voting for a radical right party by cohort and education (twoway interaction)
</figcaption>
</figure>
</div>
</div>
</div>
</section>
<section id="voting-for-a-radical-right-party-by-education-and-ess-round" class="level2" data-number="4.2">
<h2 data-number="4.2" class="anchored" data-anchor-id="voting-for-a-radical-right-party-by-education-and-ess-round"><span class="header-section-number">4.2</span> Voting for a radical right party by education and ESS round</h2>
<div class="cell">
<details class="code-fold">
<summary>Show the code</summary>
<div class="sourceCode cell-code" id="cb17"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a><span class="co"># predicted probabilities: by period</span></span>
<span id="cb17-2"><a href="#cb17-2" aria-hidden="true" tabindex="-1"></a>pp_two_rr_period <span class="ot">&lt;-</span> <span class="fu">predictions</span>(two_rr,</span>
<span id="cb17-3"><a href="#cb17-3" aria-hidden="true" tabindex="-1"></a>  <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&quot;essround&quot;</span>, <span class="st">&quot;edu_fct&quot;</span>),</span>
<span id="cb17-4"><a href="#cb17-4" aria-hidden="true" tabindex="-1"></a>  <span class="at">newdata =</span> <span class="fu">datagrid</span>(</span>
<span id="cb17-5"><a href="#cb17-5" aria-hidden="true" tabindex="-1"></a>    <span class="at">essround =</span> <span class="fu">c</span>(</span>
<span id="cb17-6"><a href="#cb17-6" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;1&quot;</span>,</span>
<span id="cb17-7"><a href="#cb17-7" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;2&quot;</span>,</span>
<span id="cb17-8"><a href="#cb17-8" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;3&quot;</span>,</span>
<span id="cb17-9"><a href="#cb17-9" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;4&quot;</span>,</span>
<span id="cb17-10"><a href="#cb17-10" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;5&quot;</span>,</span>
<span id="cb17-11"><a href="#cb17-11" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;6&quot;</span>,</span>
<span id="cb17-12"><a href="#cb17-12" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;7&quot;</span>,</span>
<span id="cb17-13"><a href="#cb17-13" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;8&quot;</span>,</span>
<span id="cb17-14"><a href="#cb17-14" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;9&quot;</span>,</span>
<span id="cb17-15"><a href="#cb17-15" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;10&quot;</span></span>
<span id="cb17-16"><a href="#cb17-16" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb17-17"><a href="#cb17-17" aria-hidden="true" tabindex="-1"></a>    <span class="at">edu_fct =</span> <span class="fu">c</span>(</span>
<span id="cb17-18"><a href="#cb17-18" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;(below) lower secondary&quot;</span>,</span>
<span id="cb17-19"><a href="#cb17-19" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;upper secondary&quot;</span>,</span>
<span id="cb17-20"><a href="#cb17-20" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;tertiary&quot;</span></span>
<span id="cb17-21"><a href="#cb17-21" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb17-22"><a href="#cb17-22" aria-hidden="true" tabindex="-1"></a>    <span class="at">grid_type =</span> <span class="st">&quot;counterfactual&quot;</span></span>
<span id="cb17-23"><a href="#cb17-23" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb17-24"><a href="#cb17-24" aria-hidden="true" tabindex="-1"></a>    <span class="at">re.form =</span> <span class="cn">NA</span></span>
<span id="cb17-25"><a href="#cb17-25" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb17-26"><a href="#cb17-26" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-27"><a href="#cb17-27" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(</span>
<span id="cb17-28"><a href="#cb17-28" aria-hidden="true" tabindex="-1"></a>  <span class="at">data =</span> pp_two_rr_period,</span>
<span id="cb17-29"><a href="#cb17-29" aria-hidden="true" tabindex="-1"></a>  <span class="fu">aes</span>(</span>
<span id="cb17-30"><a href="#cb17-30" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> essround, <span class="at">y =</span> estimate, <span class="at">ymin =</span> conf.low,</span>
<span id="cb17-31"><a href="#cb17-31" aria-hidden="true" tabindex="-1"></a>    <span class="at">ymax =</span> conf.high, <span class="at">color =</span> edu_fct</span>
<span id="cb17-32"><a href="#cb17-32" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb17-33"><a href="#cb17-33" aria-hidden="true" tabindex="-1"></a>) <span class="sc">+</span></span>
<span id="cb17-34"><a href="#cb17-34" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_pointrange</span>(<span class="at">position =</span> <span class="fu">position_dodge</span>(<span class="at">width =</span> <span class="fu">c</span>(<span class="fl">0.2</span>))) <span class="sc">+</span></span>
<span id="cb17-35"><a href="#cb17-35" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb17-36"><a href="#cb17-36" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> <span class="cn">NULL</span>, <span class="at">y =</span> <span class="st">&quot;Predicted probability&quot;</span>,</span>
<span id="cb17-37"><a href="#cb17-37" aria-hidden="true" tabindex="-1"></a>    <span class="at">color =</span> <span class="cn">NULL</span></span>
<span id="cb17-38"><a href="#cb17-38" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb17-39"><a href="#cb17-39" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_viridis_d</span>(<span class="at">option =</span> <span class="st">&quot;plasma&quot;</span>, <span class="at">end =</span> .<span class="dv">8</span>) <span class="sc">+</span></span>
<span id="cb17-40"><a href="#cb17-40" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_light</span>() <span class="sc">+</span></span>
<span id="cb17-41"><a href="#cb17-41" aria-hidden="true" tabindex="-1"></a>  <span class="fu">my_theme</span>() <span class="sc">+</span></span>
<span id="cb17-42"><a href="#cb17-42" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;top&quot;</span>)</span>
<span id="cb17-43"><a href="#cb17-43" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb17-44"><a href="#cb17-44" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(</span>
<span id="cb17-45"><a href="#cb17-45" aria-hidden="true" tabindex="-1"></a>  <span class="at">filename =</span> <span class="fu">here</span>(<span class="st">&quot;figs&quot;</span>, <span class="st">&quot;final&quot;</span>, <span class="st">&quot;appendix_fig_d6.png&quot;</span>),</span>
<span id="cb17-46"><a href="#cb17-46" aria-hidden="true" tabindex="-1"></a>  <span class="at">units =</span> <span class="fu">c</span>(<span class="st">&quot;in&quot;</span>),</span>
<span id="cb17-47"><a href="#cb17-47" aria-hidden="true" tabindex="-1"></a>  <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">5</span>,</span>
<span id="cb17-48"><a href="#cb17-48" aria-hidden="true" tabindex="-1"></a>  <span class="at">dpi =</span> <span class="dv">300</span></span>
<span id="cb17-49"><a href="#cb17-49" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
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<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-fig_d6-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
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" class="img-fluid figure-img" width="768">
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-fig_d6-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure 13: Predicted probabilities of voting for a radical right party by education and ESS wave (Appendix)
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</figure>
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</section>
<section id="voting-for-a-radical-right-party-by-education-and-age-group" class="level2" data-number="4.3">
<h2 data-number="4.3" class="anchored" data-anchor-id="voting-for-a-radical-right-party-by-education-and-age-group"><span class="header-section-number">4.3</span> Voting for a radical right party by education and age group</h2>
<div class="cell">
<details class="code-fold">
<summary>Show the code</summary>
<div class="sourceCode cell-code" id="cb18"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a><span class="co"># predicted probabilities: by age</span></span>
<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a>pp_two_rr_age <span class="ot">&lt;-</span> <span class="fu">predictions</span>(two_rr,</span>
<span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a>  <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&quot;age_grp_fct&quot;</span>, <span class="st">&quot;edu_fct&quot;</span>),</span>
<span id="cb18-4"><a href="#cb18-4" aria-hidden="true" tabindex="-1"></a>  <span class="at">newdata =</span> <span class="fu">datagrid</span>(</span>
<span id="cb18-5"><a href="#cb18-5" aria-hidden="true" tabindex="-1"></a>    <span class="at">age_grp_fct =</span> <span class="fu">c</span>(<span class="st">&quot;22-29&quot;</span>, <span class="st">&quot;30-65&quot;</span>, <span class="st">&quot;above 65&quot;</span>),</span>
<span id="cb18-6"><a href="#cb18-6" aria-hidden="true" tabindex="-1"></a>    <span class="at">edu_fct =</span> <span class="fu">c</span>(</span>
<span id="cb18-7"><a href="#cb18-7" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;(below) lower secondary&quot;</span>,</span>
<span id="cb18-8"><a href="#cb18-8" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;upper secondary&quot;</span>,</span>
<span id="cb18-9"><a href="#cb18-9" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;tertiary&quot;</span></span>
<span id="cb18-10"><a href="#cb18-10" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb18-11"><a href="#cb18-11" aria-hidden="true" tabindex="-1"></a>    <span class="at">grid_type =</span> <span class="st">&quot;counterfactual&quot;</span></span>
<span id="cb18-12"><a href="#cb18-12" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb18-13"><a href="#cb18-13" aria-hidden="true" tabindex="-1"></a>    <span class="at">re.form =</span> <span class="cn">NA</span></span>
<span id="cb18-14"><a href="#cb18-14" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb18-15"><a href="#cb18-15" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-16"><a href="#cb18-16" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(</span>
<span id="cb18-17"><a href="#cb18-17" aria-hidden="true" tabindex="-1"></a>  <span class="at">data =</span> pp_two_rr_age,</span>
<span id="cb18-18"><a href="#cb18-18" aria-hidden="true" tabindex="-1"></a>  <span class="fu">aes</span>(</span>
<span id="cb18-19"><a href="#cb18-19" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> age_grp_fct, <span class="at">y =</span> estimate, <span class="at">ymin =</span> conf.low,</span>
<span id="cb18-20"><a href="#cb18-20" aria-hidden="true" tabindex="-1"></a>    <span class="at">ymax =</span> conf.high, <span class="at">color =</span> edu_fct</span>
<span id="cb18-21"><a href="#cb18-21" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb18-22"><a href="#cb18-22" aria-hidden="true" tabindex="-1"></a>) <span class="sc">+</span></span>
<span id="cb18-23"><a href="#cb18-23" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_pointrange</span>(<span class="at">position =</span> <span class="fu">position_dodge</span>(<span class="at">width =</span> <span class="fu">c</span>(<span class="fl">0.2</span>))) <span class="sc">+</span></span>
<span id="cb18-24"><a href="#cb18-24" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb18-25"><a href="#cb18-25" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> <span class="cn">NULL</span>, <span class="at">y =</span> <span class="st">&quot;Predicted probability&quot;</span>,</span>
<span id="cb18-26"><a href="#cb18-26" aria-hidden="true" tabindex="-1"></a>    <span class="at">color =</span> <span class="cn">NULL</span></span>
<span id="cb18-27"><a href="#cb18-27" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb18-28"><a href="#cb18-28" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_viridis_d</span>(<span class="at">option =</span> <span class="st">&quot;plasma&quot;</span>, <span class="at">end =</span> .<span class="dv">8</span>) <span class="sc">+</span></span>
<span id="cb18-29"><a href="#cb18-29" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_light</span>() <span class="sc">+</span></span>
<span id="cb18-30"><a href="#cb18-30" aria-hidden="true" tabindex="-1"></a>  <span class="fu">my_theme</span>() <span class="sc">+</span></span>
<span id="cb18-31"><a href="#cb18-31" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;top&quot;</span>)</span>
<span id="cb18-32"><a href="#cb18-32" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb18-33"><a href="#cb18-33" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(</span>
<span id="cb18-34"><a href="#cb18-34" aria-hidden="true" tabindex="-1"></a>  <span class="at">filename =</span> <span class="fu">here</span>(<span class="st">&quot;figs&quot;</span>, <span class="st">&quot;final&quot;</span>, <span class="st">&quot;appendix_fig_d7.png&quot;</span>),</span>
<span id="cb18-35"><a href="#cb18-35" aria-hidden="true" tabindex="-1"></a>  <span class="at">units =</span> <span class="fu">c</span>(<span class="st">&quot;in&quot;</span>),</span>
<span id="cb18-36"><a href="#cb18-36" aria-hidden="true" tabindex="-1"></a>  <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">5</span>,</span>
<span id="cb18-37"><a href="#cb18-37" aria-hidden="true" tabindex="-1"></a>  <span class="at">dpi =</span> <span class="dv">300</span></span>
<span id="cb18-38"><a href="#cb18-38" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
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" class="img-fluid figure-img" width="768">
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-fig_d7-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure 14: Predicted probabilities of voting for a radical right party by education and age group (Appendix)
</figcaption>
</figure>
</div>
</div>
</div>
</section>
<section id="multilevel-model-voting-for-radical-right-parties-three-way-interaction" class="level2" data-number="4.4">
<h2 data-number="4.4" class="anchored" data-anchor-id="multilevel-model-voting-for-radical-right-parties-three-way-interaction"><span class="header-section-number">4.4</span> Multilevel model: Voting for radical right parties (three-way interaction)</h2>
<div class="cell">
<details class="code-fold">
<summary>Show the code</summary>
<div class="sourceCode cell-code" id="cb19"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a><span class="co"># model</span></span>
<span id="cb19-2"><a href="#cb19-2" aria-hidden="true" tabindex="-1"></a>three_rr <span class="ot">&lt;-</span> <span class="fu">glmer</span>(</span>
<span id="cb19-3"><a href="#cb19-3" aria-hidden="true" tabindex="-1"></a>  vote_radright <span class="sc">~</span> cohort_fct <span class="sc">*</span> edu_fct <span class="sc">*</span> gndr_fct <span class="sc">+</span> age_grp_fct <span class="sc">*</span> edu_fct <span class="sc">*</span> gndr_fct <span class="sc">+</span></span>
<span id="cb19-4"><a href="#cb19-4" aria-hidden="true" tabindex="-1"></a>    immigrant <span class="sc">+</span> <span class="fu">factor</span>(essround) <span class="sc">*</span> edu_fct <span class="sc">*</span> gndr_fct <span class="sc">+</span> <span class="fu">factor</span>(cntry_id) <span class="sc">+</span></span>
<span id="cb19-5"><a href="#cb19-5" aria-hidden="true" tabindex="-1"></a>    (<span class="dv">1</span> <span class="sc">|</span> cntry_wave_id),</span>
<span id="cb19-6"><a href="#cb19-6" aria-hidden="true" tabindex="-1"></a>  <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">&quot;logit&quot;</span>),</span>
<span id="cb19-7"><a href="#cb19-7" aria-hidden="true" tabindex="-1"></a>  <span class="at">nAGQ =</span> <span class="dv">0</span>,</span>
<span id="cb19-8"><a href="#cb19-8" aria-hidden="true" tabindex="-1"></a>  <span class="at">data =</span> dfradicalright</span>
<span id="cb19-9"><a href="#cb19-9" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb19-10"><a href="#cb19-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb19-11"><a href="#cb19-11" aria-hidden="true" tabindex="-1"></a><span class="co"># predicted probabilities</span></span>
<span id="cb19-12"><a href="#cb19-12" aria-hidden="true" tabindex="-1"></a>pp_three_rr <span class="ot">&lt;-</span> <span class="fu">predictions</span>(three_rr,</span>
<span id="cb19-13"><a href="#cb19-13" aria-hidden="true" tabindex="-1"></a>  <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&quot;gndr_fct&quot;</span>, <span class="st">&quot;cohort_fct&quot;</span>, <span class="st">&quot;edu_fct&quot;</span>),</span>
<span id="cb19-14"><a href="#cb19-14" aria-hidden="true" tabindex="-1"></a>  <span class="at">newdata =</span> <span class="fu">datagrid</span>(</span>
<span id="cb19-15"><a href="#cb19-15" aria-hidden="true" tabindex="-1"></a>    <span class="at">gndr_fct =</span> <span class="fu">c</span>(<span class="st">&quot;men&quot;</span>, <span class="st">&quot;women&quot;</span>),</span>
<span id="cb19-16"><a href="#cb19-16" aria-hidden="true" tabindex="-1"></a>    <span class="at">cohort_fct =</span> <span class="fu">c</span>(</span>
<span id="cb19-17"><a href="#cb19-17" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;interwar&quot;</span>,</span>
<span id="cb19-18"><a href="#cb19-18" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;post-war&quot;</span>,</span>
<span id="cb19-19"><a href="#cb19-19" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;60-70s&quot;</span>,</span>
<span id="cb19-20"><a href="#cb19-20" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;80s&quot;</span>,</span>
<span id="cb19-21"><a href="#cb19-21" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;90s&quot;</span>,</span>
<span id="cb19-22"><a href="#cb19-22" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;millennial&quot;</span></span>
<span id="cb19-23"><a href="#cb19-23" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb19-24"><a href="#cb19-24" aria-hidden="true" tabindex="-1"></a>    <span class="at">edu_fct =</span> <span class="fu">c</span>(</span>
<span id="cb19-25"><a href="#cb19-25" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;(below) lower secondary&quot;</span>,</span>
<span id="cb19-26"><a href="#cb19-26" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;upper secondary&quot;</span>,</span>
<span id="cb19-27"><a href="#cb19-27" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;tertiary&quot;</span></span>
<span id="cb19-28"><a href="#cb19-28" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb19-29"><a href="#cb19-29" aria-hidden="true" tabindex="-1"></a>    <span class="at">grid_type =</span> <span class="st">&quot;counterfactual&quot;</span></span>
<span id="cb19-30"><a href="#cb19-30" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb19-31"><a href="#cb19-31" aria-hidden="true" tabindex="-1"></a>    <span class="at">re.form =</span> <span class="cn">NA</span></span>
<span id="cb19-32"><a href="#cb19-32" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb19-33"><a href="#cb19-33" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb19-34"><a href="#cb19-34" aria-hidden="true" tabindex="-1"></a>plot_pp_three_rr <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(</span>
<span id="cb19-35"><a href="#cb19-35" aria-hidden="true" tabindex="-1"></a>  <span class="at">data =</span> pp_three_rr,</span>
<span id="cb19-36"><a href="#cb19-36" aria-hidden="true" tabindex="-1"></a>  <span class="fu">aes</span>(</span>
<span id="cb19-37"><a href="#cb19-37" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> cohort_fct, <span class="at">y =</span> estimate, <span class="at">ymin =</span> conf.low,</span>
<span id="cb19-38"><a href="#cb19-38" aria-hidden="true" tabindex="-1"></a>    <span class="at">ymax =</span> conf.high, <span class="at">color =</span> edu_fct</span>
<span id="cb19-39"><a href="#cb19-39" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb19-40"><a href="#cb19-40" aria-hidden="true" tabindex="-1"></a>) <span class="sc">+</span></span>
<span id="cb19-41"><a href="#cb19-41" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept =</span> <span class="dv">0</span>, <span class="at">color =</span> <span class="st">&quot;grey70&quot;</span>) <span class="sc">+</span></span>
<span id="cb19-42"><a href="#cb19-42" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_pointrange</span>(<span class="at">position =</span> <span class="fu">position_dodge</span>(<span class="at">width =</span> <span class="fu">c</span>(<span class="fl">0.2</span>))) <span class="sc">+</span></span>
<span id="cb19-43"><a href="#cb19-43" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb19-44"><a href="#cb19-44" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> <span class="cn">NULL</span>, <span class="at">y =</span> <span class="st">&quot;Predicted probability&quot;</span>,</span>
<span id="cb19-45"><a href="#cb19-45" aria-hidden="true" tabindex="-1"></a>    <span class="at">color =</span> <span class="cn">NULL</span></span>
<span id="cb19-46"><a href="#cb19-46" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb19-47"><a href="#cb19-47" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_viridis_d</span>(<span class="at">option =</span> <span class="st">&quot;plasma&quot;</span>, <span class="at">end =</span> .<span class="dv">8</span>) <span class="sc">+</span></span>
<span id="cb19-48"><a href="#cb19-48" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_light</span>() <span class="sc">+</span></span>
<span id="cb19-49"><a href="#cb19-49" aria-hidden="true" tabindex="-1"></a>  <span class="fu">my_theme</span>() <span class="sc">+</span></span>
<span id="cb19-50"><a href="#cb19-50" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;top&quot;</span>) <span class="sc">+</span></span>
<span id="cb19-51"><a href="#cb19-51" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>gndr_fct)</span>
<span id="cb19-52"><a href="#cb19-52" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb19-53"><a href="#cb19-53" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(</span>
<span id="cb19-54"><a href="#cb19-54" aria-hidden="true" tabindex="-1"></a>  <span class="at">filename =</span> <span class="fu">here</span>(<span class="st">&quot;figs&quot;</span>, <span class="st">&quot;final&quot;</span>, <span class="st">&quot;fig_4_upper.png&quot;</span>),</span>
<span id="cb19-55"><a href="#cb19-55" aria-hidden="true" tabindex="-1"></a>  <span class="at">units =</span> <span class="fu">c</span>(<span class="st">&quot;in&quot;</span>),</span>
<span id="cb19-56"><a href="#cb19-56" aria-hidden="true" tabindex="-1"></a>  <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">5</span>,</span>
<span id="cb19-57"><a href="#cb19-57" aria-hidden="true" tabindex="-1"></a>  <span class="at">dpi =</span> <span class="dv">300</span></span>
<span id="cb19-58"><a href="#cb19-58" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb19-59"><a href="#cb19-59" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb19-60"><a href="#cb19-60" aria-hidden="true" tabindex="-1"></a><span class="co"># marginal effects</span></span>
<span id="cb19-61"><a href="#cb19-61" aria-hidden="true" tabindex="-1"></a>me_three_rr_edu <span class="ot">&lt;-</span> <span class="fu">avg_slopes</span>(</span>
<span id="cb19-62"><a href="#cb19-62" aria-hidden="true" tabindex="-1"></a>  three_rr,</span>
<span id="cb19-63"><a href="#cb19-63" aria-hidden="true" tabindex="-1"></a>  <span class="at">variables =</span> <span class="st">&quot;edu_fct&quot;</span>,</span>
<span id="cb19-64"><a href="#cb19-64" aria-hidden="true" tabindex="-1"></a>  <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&quot;cohort_fct&quot;</span>, <span class="st">&quot;gndr_fct&quot;</span>),</span>
<span id="cb19-65"><a href="#cb19-65" aria-hidden="true" tabindex="-1"></a>  <span class="at">newdata =</span> <span class="fu">datagrid</span>(</span>
<span id="cb19-66"><a href="#cb19-66" aria-hidden="true" tabindex="-1"></a>    <span class="at">gndr_fct =</span> <span class="fu">c</span>(<span class="st">&quot;men&quot;</span>, <span class="st">&quot;women&quot;</span>),</span>
<span id="cb19-67"><a href="#cb19-67" aria-hidden="true" tabindex="-1"></a>    <span class="at">cohort_fct =</span> <span class="fu">c</span>(</span>
<span id="cb19-68"><a href="#cb19-68" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;interwar&quot;</span>,</span>
<span id="cb19-69"><a href="#cb19-69" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;post-war&quot;</span>,</span>
<span id="cb19-70"><a href="#cb19-70" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;60-70s&quot;</span>,</span>
<span id="cb19-71"><a href="#cb19-71" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;80s&quot;</span>,</span>
<span id="cb19-72"><a href="#cb19-72" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;90s&quot;</span>,</span>
<span id="cb19-73"><a href="#cb19-73" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;millennial&quot;</span></span>
<span id="cb19-74"><a href="#cb19-74" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb19-75"><a href="#cb19-75" aria-hidden="true" tabindex="-1"></a>    <span class="at">edu_fct =</span> <span class="fu">c</span>(</span>
<span id="cb19-76"><a href="#cb19-76" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;(below) lower secondary&quot;</span>,</span>
<span id="cb19-77"><a href="#cb19-77" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;upper secondary&quot;</span>,</span>
<span id="cb19-78"><a href="#cb19-78" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;tertiary&quot;</span></span>
<span id="cb19-79"><a href="#cb19-79" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb19-80"><a href="#cb19-80" aria-hidden="true" tabindex="-1"></a>    <span class="at">grid_type =</span> <span class="st">&quot;counterfactual&quot;</span></span>
<span id="cb19-81"><a href="#cb19-81" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb19-82"><a href="#cb19-82" aria-hidden="true" tabindex="-1"></a>    <span class="at">re.form =</span> <span class="cn">NA</span></span>
<span id="cb19-83"><a href="#cb19-83" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb19-84"><a href="#cb19-84" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb19-85"><a href="#cb19-85" aria-hidden="true" tabindex="-1"></a>me_three_rr_edu <span class="ot">&lt;-</span> me_three_rr_edu <span class="sc">%&gt;%</span></span>
<span id="cb19-86"><a href="#cb19-86" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">contrast =</span> <span class="fu">case_when</span>(</span>
<span id="cb19-87"><a href="#cb19-87" aria-hidden="true" tabindex="-1"></a>    contrast <span class="sc">==</span> <span class="st">&quot;tertiary - (below) lower secondary&quot;</span> <span class="sc">~</span></span>
<span id="cb19-88"><a href="#cb19-88" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;high vs low edu&quot;</span>,</span>
<span id="cb19-89"><a href="#cb19-89" aria-hidden="true" tabindex="-1"></a>    contrast <span class="sc">==</span> <span class="st">&quot;upper secondary - (below) lower secondary&quot;</span> <span class="sc">~</span></span>
<span id="cb19-90"><a href="#cb19-90" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;medium vs low edu&quot;</span></span>
<span id="cb19-91"><a href="#cb19-91" aria-hidden="true" tabindex="-1"></a>  ))</span>
<span id="cb19-92"><a href="#cb19-92" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb19-93"><a href="#cb19-93" aria-hidden="true" tabindex="-1"></a>plot_me_three_rr <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(<span class="at">data =</span> me_three_rr_edu, <span class="fu">aes</span>(</span>
<span id="cb19-94"><a href="#cb19-94" aria-hidden="true" tabindex="-1"></a>  <span class="at">x =</span> cohort_fct, <span class="at">y =</span> estimate,</span>
<span id="cb19-95"><a href="#cb19-95" aria-hidden="true" tabindex="-1"></a>  <span class="at">ymin =</span> conf.low, <span class="at">ymax =</span> conf.high,</span>
<span id="cb19-96"><a href="#cb19-96" aria-hidden="true" tabindex="-1"></a>  <span class="at">color =</span> contrast</span>
<span id="cb19-97"><a href="#cb19-97" aria-hidden="true" tabindex="-1"></a>)) <span class="sc">+</span></span>
<span id="cb19-98"><a href="#cb19-98" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept =</span> <span class="dv">0</span>, <span class="at">color =</span> <span class="st">&quot;grey70&quot;</span>) <span class="sc">+</span></span>
<span id="cb19-99"><a href="#cb19-99" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_pointrange</span>(<span class="at">position =</span> <span class="fu">position_dodge</span>(<span class="at">width =</span> <span class="fu">c</span>(<span class="fl">0.2</span>))) <span class="sc">+</span></span>
<span id="cb19-100"><a href="#cb19-100" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb19-101"><a href="#cb19-101" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> <span class="cn">NULL</span>, <span class="at">color =</span> <span class="st">&quot;Education&quot;</span>,</span>
<span id="cb19-102"><a href="#cb19-102" aria-hidden="true" tabindex="-1"></a>    <span class="at">y =</span> <span class="st">&quot;Marginal effect of education&quot;</span></span>
<span id="cb19-103"><a href="#cb19-103" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb19-104"><a href="#cb19-104" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;#fe9f6d&quot;</span>, <span class="st">&quot;#8c2981&quot;</span>)) <span class="sc">+</span></span>
<span id="cb19-105"><a href="#cb19-105" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>gndr_fct) <span class="sc">+</span></span>
<span id="cb19-106"><a href="#cb19-106" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_light</span>() <span class="sc">+</span></span>
<span id="cb19-107"><a href="#cb19-107" aria-hidden="true" tabindex="-1"></a>  <span class="fu">my_theme</span>() <span class="sc">+</span></span>
<span id="cb19-108"><a href="#cb19-108" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;top&quot;</span>)</span>
<span id="cb19-109"><a href="#cb19-109" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb19-110"><a href="#cb19-110" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(</span>
<span id="cb19-111"><a href="#cb19-111" aria-hidden="true" tabindex="-1"></a>  <span class="at">filename =</span> <span class="fu">here</span>(<span class="st">&quot;figs&quot;</span>, <span class="st">&quot;final&quot;</span>, <span class="st">&quot;fig_4_lower.png&quot;</span>),</span>
<span id="cb19-112"><a href="#cb19-112" aria-hidden="true" tabindex="-1"></a>  <span class="at">units =</span> <span class="fu">c</span>(<span class="st">&quot;in&quot;</span>),</span>
<span id="cb19-113"><a href="#cb19-113" aria-hidden="true" tabindex="-1"></a>  <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">5</span>,</span>
<span id="cb19-114"><a href="#cb19-114" aria-hidden="true" tabindex="-1"></a>  <span class="at">dpi =</span> <span class="dv">300</span></span>
<span id="cb19-115"><a href="#cb19-115" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb19-116"><a href="#cb19-116" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb19-117"><a href="#cb19-117" aria-hidden="true" tabindex="-1"></a><span class="co"># Combine the plots vertically</span></span>
<span id="cb19-118"><a href="#cb19-118" aria-hidden="true" tabindex="-1"></a>combined_plot <span class="ot">&lt;-</span> plot_pp_three_rr <span class="sc">/</span> plot_me_three_rr</span>
<span id="cb19-119"><a href="#cb19-119" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb19-120"><a href="#cb19-120" aria-hidden="true" tabindex="-1"></a><span class="co"># Save the combined plot</span></span>
<span id="cb19-121"><a href="#cb19-121" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(</span>
<span id="cb19-122"><a href="#cb19-122" aria-hidden="true" tabindex="-1"></a>  <span class="at">filename =</span> <span class="fu">here</span>(<span class="st">&quot;figs&quot;</span>, <span class="st">&quot;final&quot;</span>, <span class="st">&quot;figure_4.png&quot;</span>),</span>
<span id="cb19-123"><a href="#cb19-123" aria-hidden="true" tabindex="-1"></a>  <span class="at">plot =</span> combined_plot,</span>
<span id="cb19-124"><a href="#cb19-124" aria-hidden="true" tabindex="-1"></a>  <span class="at">units =</span> <span class="fu">c</span>(<span class="st">&quot;in&quot;</span>),</span>
<span id="cb19-125"><a href="#cb19-125" aria-hidden="true" tabindex="-1"></a>  <span class="at">width =</span> <span class="dv">8</span>,</span>
<span id="cb19-126"><a href="#cb19-126" aria-hidden="true" tabindex="-1"></a>  <span class="at">height =</span> <span class="dv">10</span>,</span>
<span id="cb19-127"><a href="#cb19-127" aria-hidden="true" tabindex="-1"></a>  <span class="at">dpi =</span> <span class="dv">300</span></span>
<span id="cb19-128"><a href="#cb19-128" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb19-129"><a href="#cb19-129" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb19-130"><a href="#cb19-130" aria-hidden="true" tabindex="-1"></a>combined_plot</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<div id="fig-fig_4" class="quarto-float quarto-figure quarto-figure-center anchored">
<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-fig_4-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<img role="img" 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" class="img-fluid figure-img" width="768">
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-fig_4-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure 15: Predicted probabilities and marginal effects of voting for a radical right party
</figcaption>
</figure>
</div>
</div>
</div>
</section>
<section id="table-with-predicted-probabilities-of-voting-for-the-radical-right" class="level2" data-number="4.5">
<h2 data-number="4.5" class="anchored" data-anchor-id="table-with-predicted-probabilities-of-voting-for-the-radical-right"><span class="header-section-number">4.5</span> Table with predicted probabilities of voting for the radical right</h2>
<div class="cell">
<details class="code-fold">
<summary>Show the code</summary>
<div class="sourceCode cell-code" id="cb20"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" aria-hidden="true" tabindex="-1"></a>pp_three_rr <span class="sc">%&gt;%</span> </span>
<span id="cb20-2"><a href="#cb20-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">select</span>(cohort_fct<span class="sc">:</span>p.value) <span class="sc">%&gt;%</span> </span>
<span id="cb20-3"><a href="#cb20-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">rename</span>(<span class="at">cohort =</span> cohort_fct) <span class="sc">%&gt;%</span> </span>
<span id="cb20-4"><a href="#cb20-4" aria-hidden="true" tabindex="-1"></a><span class="fu">gt</span>() <span class="sc">%&gt;%</span> </span>
<span id="cb20-5"><a href="#cb20-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">fmt_number</span>(</span>
<span id="cb20-6"><a href="#cb20-6" aria-hidden="true" tabindex="-1"></a>    <span class="at">decimals =</span> <span class="dv">3</span></span>
<span id="cb20-7"><a href="#cb20-7" aria-hidden="true" tabindex="-1"></a>  )</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div id="tbl-table_pp_rr" class="cell quarto-float quarto-figure quarto-figure-center anchored">
<figure class="quarto-float quarto-float-tbl figure">
<figcaption class="quarto-float-caption-top quarto-float-caption quarto-float-tbl" id="tbl-table_pp_rr-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Table 2: Predicted probabilities of voting for the radical right (threeway interaction)
</figcaption>
<div aria-describedby="tbl-table_pp_rr-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<div class="cell-output-display">
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</style>

<table class="gt_table do-not-create-environment cell caption-top table table-sm table-striped small" data-quarto-postprocess="true" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
<thead>
<tr class="header gt_col_headings">
<th id="cohort" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col">cohort</th>
<th id="edu_fct" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col">edu_fct</th>
<th id="estimate" class="gt_col_heading gt_columns_bottom_border gt_right" data-quarto-table-cell-role="th" scope="col">estimate</th>
<th id="std.error" class="gt_col_heading gt_columns_bottom_border gt_right" data-quarto-table-cell-role="th" scope="col">std.error</th>
<th id="statistic" class="gt_col_heading gt_columns_bottom_border gt_right" data-quarto-table-cell-role="th" scope="col">statistic</th>
<th id="p.value" class="gt_col_heading gt_columns_bottom_border gt_right" data-quarto-table-cell-role="th" scope="col">p.value</th>
</tr>
</thead>
<tbody class="gt_table_body">
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">interwar</td>
<td class="gt_row gt_center" headers="edu_fct">(below) lower secondary</td>
<td class="gt_row gt_right" headers="estimate">0.089</td>
<td class="gt_row gt_right" headers="std.error">0.017</td>
<td class="gt_row gt_right" headers="statistic">5.106</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">interwar</td>
<td class="gt_row gt_center" headers="edu_fct">upper secondary</td>
<td class="gt_row gt_right" headers="estimate">0.067</td>
<td class="gt_row gt_right" headers="std.error">0.015</td>
<td class="gt_row gt_right" headers="statistic">4.382</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">interwar</td>
<td class="gt_row gt_center" headers="edu_fct">tertiary</td>
<td class="gt_row gt_right" headers="estimate">0.075</td>
<td class="gt_row gt_right" headers="std.error">0.019</td>
<td class="gt_row gt_right" headers="statistic">3.941</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">post-war</td>
<td class="gt_row gt_center" headers="edu_fct">(below) lower secondary</td>
<td class="gt_row gt_right" headers="estimate">0.104</td>
<td class="gt_row gt_right" headers="std.error">0.013</td>
<td class="gt_row gt_right" headers="statistic">8.080</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">post-war</td>
<td class="gt_row gt_center" headers="edu_fct">upper secondary</td>
<td class="gt_row gt_right" headers="estimate">0.077</td>
<td class="gt_row gt_right" headers="std.error">0.010</td>
<td class="gt_row gt_right" headers="statistic">8.029</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">post-war</td>
<td class="gt_row gt_center" headers="edu_fct">tertiary</td>
<td class="gt_row gt_right" headers="estimate">0.049</td>
<td class="gt_row gt_right" headers="std.error">0.007</td>
<td class="gt_row gt_right" headers="statistic">6.739</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">60-70s</td>
<td class="gt_row gt_center" headers="edu_fct">(below) lower secondary</td>
<td class="gt_row gt_right" headers="estimate">0.115</td>
<td class="gt_row gt_right" headers="std.error">0.012</td>
<td class="gt_row gt_right" headers="statistic">9.446</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">60-70s</td>
<td class="gt_row gt_center" headers="edu_fct">upper secondary</td>
<td class="gt_row gt_right" headers="estimate">0.086</td>
<td class="gt_row gt_right" headers="std.error">0.009</td>
<td class="gt_row gt_right" headers="statistic">9.124</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">60-70s</td>
<td class="gt_row gt_center" headers="edu_fct">tertiary</td>
<td class="gt_row gt_right" headers="estimate">0.041</td>
<td class="gt_row gt_right" headers="std.error">0.006</td>
<td class="gt_row gt_right" headers="statistic">7.442</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">80s</td>
<td class="gt_row gt_center" headers="edu_fct">(below) lower secondary</td>
<td class="gt_row gt_right" headers="estimate">0.146</td>
<td class="gt_row gt_right" headers="std.error">0.015</td>
<td class="gt_row gt_right" headers="statistic">9.605</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">80s</td>
<td class="gt_row gt_center" headers="edu_fct">upper secondary</td>
<td class="gt_row gt_right" headers="estimate">0.104</td>
<td class="gt_row gt_right" headers="std.error">0.011</td>
<td class="gt_row gt_right" headers="statistic">9.591</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">80s</td>
<td class="gt_row gt_center" headers="edu_fct">tertiary</td>
<td class="gt_row gt_right" headers="estimate">0.043</td>
<td class="gt_row gt_right" headers="std.error">0.006</td>
<td class="gt_row gt_right" headers="statistic">7.345</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">90s</td>
<td class="gt_row gt_center" headers="edu_fct">(below) lower secondary</td>
<td class="gt_row gt_right" headers="estimate">0.149</td>
<td class="gt_row gt_right" headers="std.error">0.017</td>
<td class="gt_row gt_right" headers="statistic">8.672</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">90s</td>
<td class="gt_row gt_center" headers="edu_fct">upper secondary</td>
<td class="gt_row gt_right" headers="estimate">0.125</td>
<td class="gt_row gt_right" headers="std.error">0.013</td>
<td class="gt_row gt_right" headers="statistic">10.022</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">90s</td>
<td class="gt_row gt_center" headers="edu_fct">tertiary</td>
<td class="gt_row gt_right" headers="estimate">0.040</td>
<td class="gt_row gt_right" headers="std.error">0.006</td>
<td class="gt_row gt_right" headers="statistic">7.169</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">millennial</td>
<td class="gt_row gt_center" headers="edu_fct">(below) lower secondary</td>
<td class="gt_row gt_right" headers="estimate">0.154</td>
<td class="gt_row gt_right" headers="std.error">0.025</td>
<td class="gt_row gt_right" headers="statistic">6.262</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">millennial</td>
<td class="gt_row gt_center" headers="edu_fct">upper secondary</td>
<td class="gt_row gt_right" headers="estimate">0.121</td>
<td class="gt_row gt_right" headers="std.error">0.013</td>
<td class="gt_row gt_right" headers="statistic">9.026</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">millennial</td>
<td class="gt_row gt_center" headers="edu_fct">tertiary</td>
<td class="gt_row gt_right" headers="estimate">0.042</td>
<td class="gt_row gt_right" headers="std.error">0.007</td>
<td class="gt_row gt_right" headers="statistic">6.480</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">interwar</td>
<td class="gt_row gt_center" headers="edu_fct">(below) lower secondary</td>
<td class="gt_row gt_right" headers="estimate">0.035</td>
<td class="gt_row gt_right" headers="std.error">0.008</td>
<td class="gt_row gt_right" headers="statistic">4.220</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">interwar</td>
<td class="gt_row gt_center" headers="edu_fct">upper secondary</td>
<td class="gt_row gt_right" headers="estimate">0.036</td>
<td class="gt_row gt_right" headers="std.error">0.010</td>
<td class="gt_row gt_right" headers="statistic">3.580</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">interwar</td>
<td class="gt_row gt_center" headers="edu_fct">tertiary</td>
<td class="gt_row gt_right" headers="estimate">0.042</td>
<td class="gt_row gt_right" headers="std.error">0.019</td>
<td class="gt_row gt_right" headers="statistic">2.161</td>
<td class="gt_row gt_right" headers="p.value">0.031</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">post-war</td>
<td class="gt_row gt_center" headers="edu_fct">(below) lower secondary</td>
<td class="gt_row gt_right" headers="estimate">0.068</td>
<td class="gt_row gt_right" headers="std.error">0.009</td>
<td class="gt_row gt_right" headers="statistic">7.327</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">post-war</td>
<td class="gt_row gt_center" headers="edu_fct">upper secondary</td>
<td class="gt_row gt_right" headers="estimate">0.046</td>
<td class="gt_row gt_right" headers="std.error">0.007</td>
<td class="gt_row gt_right" headers="statistic">6.879</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">post-war</td>
<td class="gt_row gt_center" headers="edu_fct">tertiary</td>
<td class="gt_row gt_right" headers="estimate">0.032</td>
<td class="gt_row gt_right" headers="std.error">0.006</td>
<td class="gt_row gt_right" headers="statistic">5.112</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">60-70s</td>
<td class="gt_row gt_center" headers="edu_fct">(below) lower secondary</td>
<td class="gt_row gt_right" headers="estimate">0.089</td>
<td class="gt_row gt_right" headers="std.error">0.010</td>
<td class="gt_row gt_right" headers="statistic">8.823</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">60-70s</td>
<td class="gt_row gt_center" headers="edu_fct">upper secondary</td>
<td class="gt_row gt_right" headers="estimate">0.055</td>
<td class="gt_row gt_right" headers="std.error">0.007</td>
<td class="gt_row gt_right" headers="statistic">8.042</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">60-70s</td>
<td class="gt_row gt_center" headers="edu_fct">tertiary</td>
<td class="gt_row gt_right" headers="estimate">0.022</td>
<td class="gt_row gt_right" headers="std.error">0.004</td>
<td class="gt_row gt_right" headers="statistic">6.175</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">80s</td>
<td class="gt_row gt_center" headers="edu_fct">(below) lower secondary</td>
<td class="gt_row gt_right" headers="estimate">0.103</td>
<td class="gt_row gt_right" headers="std.error">0.012</td>
<td class="gt_row gt_right" headers="statistic">8.352</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">80s</td>
<td class="gt_row gt_center" headers="edu_fct">upper secondary</td>
<td class="gt_row gt_right" headers="estimate">0.073</td>
<td class="gt_row gt_right" headers="std.error">0.009</td>
<td class="gt_row gt_right" headers="statistic">8.514</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">80s</td>
<td class="gt_row gt_center" headers="edu_fct">tertiary</td>
<td class="gt_row gt_right" headers="estimate">0.021</td>
<td class="gt_row gt_right" headers="std.error">0.003</td>
<td class="gt_row gt_right" headers="statistic">6.164</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">90s</td>
<td class="gt_row gt_center" headers="edu_fct">(below) lower secondary</td>
<td class="gt_row gt_right" headers="estimate">0.153</td>
<td class="gt_row gt_right" headers="std.error">0.018</td>
<td class="gt_row gt_right" headers="statistic">8.714</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">90s</td>
<td class="gt_row gt_center" headers="edu_fct">upper secondary</td>
<td class="gt_row gt_right" headers="estimate">0.087</td>
<td class="gt_row gt_right" headers="std.error">0.010</td>
<td class="gt_row gt_right" headers="statistic">8.691</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">90s</td>
<td class="gt_row gt_center" headers="edu_fct">tertiary</td>
<td class="gt_row gt_right" headers="estimate">0.022</td>
<td class="gt_row gt_right" headers="std.error">0.004</td>
<td class="gt_row gt_right" headers="statistic">6.211</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">millennial</td>
<td class="gt_row gt_center" headers="edu_fct">(below) lower secondary</td>
<td class="gt_row gt_right" headers="estimate">0.167</td>
<td class="gt_row gt_right" headers="std.error">0.027</td>
<td class="gt_row gt_right" headers="statistic">6.167</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="odd">
<td class="gt_row gt_center" headers="cohort">millennial</td>
<td class="gt_row gt_center" headers="edu_fct">upper secondary</td>
<td class="gt_row gt_right" headers="estimate">0.101</td>
<td class="gt_row gt_right" headers="std.error">0.013</td>
<td class="gt_row gt_right" headers="statistic">7.914</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
<tr class="even">
<td class="gt_row gt_center" headers="cohort">millennial</td>
<td class="gt_row gt_center" headers="edu_fct">tertiary</td>
<td class="gt_row gt_right" headers="estimate">0.020</td>
<td class="gt_row gt_right" headers="std.error">0.004</td>
<td class="gt_row gt_right" headers="statistic">5.277</td>
<td class="gt_row gt_right" headers="p.value">0.000</td>
</tr>
</tbody>
</table>

</div>
</div>
</div>
</figure>
</div>
</div>
</section>
<section id="predicted-probabilities-of-radical-right-by-education-ess-round-and-gender" class="level2" data-number="4.6">
<h2 data-number="4.6" class="anchored" data-anchor-id="predicted-probabilities-of-radical-right-by-education-ess-round-and-gender"><span class="header-section-number">4.6</span> Predicted probabilities of radical right: by education, ESS round and gender</h2>
<div class="cell">
<details class="code-fold">
<summary>Show the code</summary>
<div class="sourceCode cell-code" id="cb21"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a>pp_three_rr_period <span class="ot">&lt;-</span> <span class="fu">predictions</span>(three_rr,</span>
<span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a>  <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&quot;gndr_fct&quot;</span>, <span class="st">&quot;essround&quot;</span>, <span class="st">&quot;edu_fct&quot;</span>),</span>
<span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a>  <span class="at">newdata =</span> <span class="fu">datagrid</span>(</span>
<span id="cb21-4"><a href="#cb21-4" aria-hidden="true" tabindex="-1"></a>    <span class="at">gndr_fct =</span> <span class="fu">c</span>(<span class="st">&quot;men&quot;</span>, <span class="st">&quot;women&quot;</span>),</span>
<span id="cb21-5"><a href="#cb21-5" aria-hidden="true" tabindex="-1"></a>    <span class="at">essround =</span> <span class="fu">c</span>(</span>
<span id="cb21-6"><a href="#cb21-6" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;1&quot;</span>,</span>
<span id="cb21-7"><a href="#cb21-7" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;2&quot;</span>,</span>
<span id="cb21-8"><a href="#cb21-8" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;3&quot;</span>,</span>
<span id="cb21-9"><a href="#cb21-9" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;4&quot;</span>,</span>
<span id="cb21-10"><a href="#cb21-10" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;5&quot;</span>,</span>
<span id="cb21-11"><a href="#cb21-11" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;6&quot;</span>,</span>
<span id="cb21-12"><a href="#cb21-12" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;7&quot;</span>,</span>
<span id="cb21-13"><a href="#cb21-13" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;8&quot;</span>,</span>
<span id="cb21-14"><a href="#cb21-14" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;9&quot;</span>,</span>
<span id="cb21-15"><a href="#cb21-15" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;10&quot;</span></span>
<span id="cb21-16"><a href="#cb21-16" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb21-17"><a href="#cb21-17" aria-hidden="true" tabindex="-1"></a>    <span class="at">edu_fct =</span> <span class="fu">c</span>(</span>
<span id="cb21-18"><a href="#cb21-18" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;(below) lower secondary&quot;</span>,</span>
<span id="cb21-19"><a href="#cb21-19" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;upper secondary&quot;</span>,</span>
<span id="cb21-20"><a href="#cb21-20" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;tertiary&quot;</span></span>
<span id="cb21-21"><a href="#cb21-21" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb21-22"><a href="#cb21-22" aria-hidden="true" tabindex="-1"></a>    <span class="at">grid_type =</span> <span class="st">&quot;counterfactual&quot;</span></span>
<span id="cb21-23"><a href="#cb21-23" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb21-24"><a href="#cb21-24" aria-hidden="true" tabindex="-1"></a>    <span class="at">re.form =</span> <span class="cn">NA</span></span>
<span id="cb21-25"><a href="#cb21-25" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb21-26"><a href="#cb21-26" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb21-27"><a href="#cb21-27" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(</span>
<span id="cb21-28"><a href="#cb21-28" aria-hidden="true" tabindex="-1"></a>  <span class="at">data =</span> pp_three_rr_period,</span>
<span id="cb21-29"><a href="#cb21-29" aria-hidden="true" tabindex="-1"></a>  <span class="fu">aes</span>(</span>
<span id="cb21-30"><a href="#cb21-30" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> essround, <span class="at">y =</span> estimate, <span class="at">ymin =</span> conf.low,</span>
<span id="cb21-31"><a href="#cb21-31" aria-hidden="true" tabindex="-1"></a>    <span class="at">ymax =</span> conf.high, <span class="at">color =</span> edu_fct</span>
<span id="cb21-32"><a href="#cb21-32" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb21-33"><a href="#cb21-33" aria-hidden="true" tabindex="-1"></a>) <span class="sc">+</span></span>
<span id="cb21-34"><a href="#cb21-34" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_pointrange</span>(<span class="at">position =</span> <span class="fu">position_dodge</span>(<span class="at">width =</span> <span class="fu">c</span>(<span class="fl">0.2</span>))) <span class="sc">+</span></span>
<span id="cb21-35"><a href="#cb21-35" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb21-36"><a href="#cb21-36" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> <span class="cn">NULL</span>, <span class="at">y =</span> <span class="st">&quot;Predicted probability&quot;</span>,</span>
<span id="cb21-37"><a href="#cb21-37" aria-hidden="true" tabindex="-1"></a>    <span class="at">color =</span> <span class="cn">NULL</span></span>
<span id="cb21-38"><a href="#cb21-38" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb21-39"><a href="#cb21-39" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_viridis_d</span>(<span class="at">option =</span> <span class="st">&quot;plasma&quot;</span>, <span class="at">end =</span> .<span class="dv">8</span>) <span class="sc">+</span></span>
<span id="cb21-40"><a href="#cb21-40" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_light</span>() <span class="sc">+</span></span>
<span id="cb21-41"><a href="#cb21-41" aria-hidden="true" tabindex="-1"></a>  <span class="fu">my_theme</span>() <span class="sc">+</span></span>
<span id="cb21-42"><a href="#cb21-42" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;top&quot;</span>) <span class="sc">+</span></span>
<span id="cb21-43"><a href="#cb21-43" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>gndr_fct)</span>
<span id="cb21-44"><a href="#cb21-44" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb21-45"><a href="#cb21-45" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(</span>
<span id="cb21-46"><a href="#cb21-46" aria-hidden="true" tabindex="-1"></a>  <span class="at">filename =</span> <span class="fu">here</span>(<span class="st">&quot;figs&quot;</span>, <span class="st">&quot;final&quot;</span>, <span class="st">&quot;appendix_fig_d8.png&quot;</span>),</span>
<span id="cb21-47"><a href="#cb21-47" aria-hidden="true" tabindex="-1"></a>  <span class="at">units =</span> <span class="fu">c</span>(<span class="st">&quot;in&quot;</span>),</span>
<span id="cb21-48"><a href="#cb21-48" aria-hidden="true" tabindex="-1"></a>  <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">5</span>,</span>
<span id="cb21-49"><a href="#cb21-49" aria-hidden="true" tabindex="-1"></a>  <span class="at">dpi =</span> <span class="dv">300</span></span>
<span id="cb21-50"><a href="#cb21-50" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<div id="fig-fig_d8" class="quarto-float quarto-figure quarto-figure-center anchored">
<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-fig_d8-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<img role="img" 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" class="img-fluid figure-img" width="768">
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-fig_d8-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure 16: Predicted probabilities of voting for a radical right party by education, ESS wave and gender (Appendix)
</figcaption>
</figure>
</div>
</div>
</div>
</section>
<section id="predicted-probabilities-of-radical-right-by-gender-cohort-and-age-group" class="level2" data-number="4.7">
<h2 data-number="4.7" class="anchored" data-anchor-id="predicted-probabilities-of-radical-right-by-gender-cohort-and-age-group"><span class="header-section-number">4.7</span> Predicted probabilities of radical right: by gender, cohort and age group</h2>
<div class="cell">
<details class="code-fold">
<summary>Show the code</summary>
<div class="sourceCode cell-code" id="cb22"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a>pp_three_rr_age <span class="ot">&lt;-</span> <span class="fu">predictions</span>(three_rr,</span>
<span id="cb22-2"><a href="#cb22-2" aria-hidden="true" tabindex="-1"></a>  <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&quot;gndr_fct&quot;</span>, <span class="st">&quot;age_grp_fct&quot;</span>, <span class="st">&quot;edu_fct&quot;</span>),</span>
<span id="cb22-3"><a href="#cb22-3" aria-hidden="true" tabindex="-1"></a>  <span class="at">newdata =</span> <span class="fu">datagrid</span>(</span>
<span id="cb22-4"><a href="#cb22-4" aria-hidden="true" tabindex="-1"></a>    <span class="at">gndr_fct =</span> <span class="fu">c</span>(<span class="st">&quot;men&quot;</span>, <span class="st">&quot;women&quot;</span>),</span>
<span id="cb22-5"><a href="#cb22-5" aria-hidden="true" tabindex="-1"></a>    <span class="at">age_grp_fct =</span> <span class="fu">c</span>(<span class="st">&quot;22-29&quot;</span>, <span class="st">&quot;30-65&quot;</span>, <span class="st">&quot;above 65&quot;</span>),</span>
<span id="cb22-6"><a href="#cb22-6" aria-hidden="true" tabindex="-1"></a>    <span class="at">edu_fct =</span> <span class="fu">c</span>(</span>
<span id="cb22-7"><a href="#cb22-7" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;(below) lower secondary&quot;</span>,</span>
<span id="cb22-8"><a href="#cb22-8" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;upper secondary&quot;</span>,</span>
<span id="cb22-9"><a href="#cb22-9" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;tertiary&quot;</span></span>
<span id="cb22-10"><a href="#cb22-10" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb22-11"><a href="#cb22-11" aria-hidden="true" tabindex="-1"></a>    <span class="at">grid_type =</span> <span class="st">&quot;counterfactual&quot;</span></span>
<span id="cb22-12"><a href="#cb22-12" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb22-13"><a href="#cb22-13" aria-hidden="true" tabindex="-1"></a>    <span class="at">re.form =</span> <span class="cn">NA</span></span>
<span id="cb22-14"><a href="#cb22-14" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb22-15"><a href="#cb22-15" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb22-16"><a href="#cb22-16" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(</span>
<span id="cb22-17"><a href="#cb22-17" aria-hidden="true" tabindex="-1"></a>  <span class="at">data =</span> pp_three_rr_age,</span>
<span id="cb22-18"><a href="#cb22-18" aria-hidden="true" tabindex="-1"></a>  <span class="fu">aes</span>(</span>
<span id="cb22-19"><a href="#cb22-19" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> age_grp_fct, <span class="at">y =</span> estimate, <span class="at">ymin =</span> conf.low,</span>
<span id="cb22-20"><a href="#cb22-20" aria-hidden="true" tabindex="-1"></a>    <span class="at">ymax =</span> conf.high, <span class="at">color =</span> edu_fct</span>
<span id="cb22-21"><a href="#cb22-21" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb22-22"><a href="#cb22-22" aria-hidden="true" tabindex="-1"></a>) <span class="sc">+</span></span>
<span id="cb22-23"><a href="#cb22-23" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_pointrange</span>(<span class="at">position =</span> <span class="fu">position_dodge</span>(<span class="at">width =</span> <span class="fu">c</span>(<span class="fl">0.2</span>))) <span class="sc">+</span></span>
<span id="cb22-24"><a href="#cb22-24" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb22-25"><a href="#cb22-25" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> <span class="cn">NULL</span>, <span class="at">y =</span> <span class="st">&quot;Predicted probability&quot;</span>,</span>
<span id="cb22-26"><a href="#cb22-26" aria-hidden="true" tabindex="-1"></a>    <span class="at">color =</span> <span class="cn">NULL</span></span>
<span id="cb22-27"><a href="#cb22-27" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb22-28"><a href="#cb22-28" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_viridis_d</span>(<span class="at">option =</span> <span class="st">&quot;plasma&quot;</span>, <span class="at">end =</span> .<span class="dv">8</span>) <span class="sc">+</span></span>
<span id="cb22-29"><a href="#cb22-29" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_light</span>() <span class="sc">+</span></span>
<span id="cb22-30"><a href="#cb22-30" aria-hidden="true" tabindex="-1"></a>  <span class="fu">my_theme</span>() <span class="sc">+</span></span>
<span id="cb22-31"><a href="#cb22-31" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;top&quot;</span>) <span class="sc">+</span></span>
<span id="cb22-32"><a href="#cb22-32" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>gndr_fct)</span>
<span id="cb22-33"><a href="#cb22-33" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb22-34"><a href="#cb22-34" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(</span>
<span id="cb22-35"><a href="#cb22-35" aria-hidden="true" tabindex="-1"></a>  <span class="at">filename =</span> <span class="fu">here</span>(<span class="st">&quot;figs&quot;</span>, <span class="st">&quot;final&quot;</span>, <span class="st">&quot;appendix_fig_d9.png&quot;</span>),</span>
<span id="cb22-36"><a href="#cb22-36" aria-hidden="true" tabindex="-1"></a>  <span class="at">units =</span> <span class="fu">c</span>(<span class="st">&quot;in&quot;</span>),</span>
<span id="cb22-37"><a href="#cb22-37" aria-hidden="true" tabindex="-1"></a>  <span class="at">width =</span> <span class="dv">8</span>, <span class="at">height =</span> <span class="dv">5</span>,</span>
<span id="cb22-38"><a href="#cb22-38" aria-hidden="true" tabindex="-1"></a>  <span class="at">dpi =</span> <span class="dv">300</span></span>
<span id="cb22-39"><a href="#cb22-39" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<div id="fig-fig_d9" class="quarto-float quarto-figure quarto-figure-center anchored">
<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-fig_d9-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<img role="img" 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wB+yCaLD4YASoYyWCGbLD4YAvBDNll8MARQMpTBCtlk8cEQgB+yyeKDIYCSoQxWyCaLD4YA/JBNFh8MAZQMZbBCNll8MATgh2yy+GAIoGQogxWyyeKDIQA/ZJPFB0MAJUMZrJBNFh8MAfghmyw+GAIoGcpghWyy+GAIwA/ZZPHBEEDJUAYrZJPFB0MAfsgmiw+GAEqGMlghmyw+GALwQzZZfDAEUDKUwQrZZPHBEIAfssnigyGAkqEMVsgmiw+GAPyQTRYfDAGUDGWwQjZZfDAE4IdssvhgCKBkKIMVssnigyEAP2STxQdDACVDGayQTRYfDAH4IZssPhgCKBnKYIVssvhgCMAP2WTxwRBAyVAGK2STxQdDAH7IJosPhgBKhjJYIZssPhgC8EM2WXwwBFAylMEK2WTxwRCAH7LJ4oMhgJKhDFbIJosPhgD8kE0WHwwBlAxlsEI2WXwwBOCHbLL4YAigZCiDFbLJ4oMhAD9kk8UHQwAlQxmskE0WHwwB+CGbLD4YAigZymCFbLL4YAjAD9lk8cEQQMlQBitkk8UHQwB+yCaLD4YASoYyWCGbLD4YAvBDNll8sGkK4Ph469f4Mlghmyw+GALwQzZZfDAEUDKUwQrZZPHBEIAfssnigyGAkqEMVsgmiw+GAPyQTRYfDAGUDGWwQjZZfDAE4IdssvhgCKBkKIMVssnigyEAP2STxQdDACVDGayQTRYfDAH4IZssPhgCKBnKYIVssvhgCMAP2WTxwRBAyVAGK2STxQdDAH7IJosPhgBKhjJYIZssPhgC8EM2WXwwBFAylMEK2WTxwRCAH7LJ4oMhgJKhDFbIJosPhgD8kE0WHwwBlAxlsEI2WXwwBOCHbLL4YAigZCiDFbLJ4oMhAD9kk8UHQwAlQxmskE0WHwwB+CGbLD4YAigZymCFbLL4YAjAD9lk8cEQQMlQBitkk8UHQwB+yCaLD4YASoYyWCGbLD4YAvBDNll8MARQMpTBCtlk8cEQgB+yyeKDIYCSoQxWyCaLD4YA/JBNFh8MAZQMZbBCNll8MATgh2yy+GAIoGQogxWyyeKDIQA/ZJPFB0MAJUMZrJBNFh8MAfghmyw+GAIoGcpghWyy+GAIwA/ZZPHBEEDJUAYrZJPFB0MAfsgmiw+GAEqGMlghmyw+GALwQzZZfDAEUDKUwQrZZPHBEIAfssnigyGAkqEMVsgmiw+GAPyQTRYfDAGUDGWwQjZZfDAE4IdssvhgCKBkKIMVssnigyEAP2STxQdDACVDGayQTRYfDAH4IZssPhgCKBnKYIVssvhgCMAP2WTxwRBAyVAGK2STxQdDAH7IJosPhgBKhjJYIZssPhgC8EM2WXwwBFAylMEK2WTxwRCAH7LJ4oMhgJKhDFbIJosPhgD8kE0WHwwBlAxlsEI2WXwwBOCHbLL4YAigZCiDFbLJ4oMhAD9kk8UHQwAlQxmskE0WHwwB+CGbLD4YAigZymCFbLL4YAjAD9lk8cEQQMlQBitkk8UHQwB+yCaLD4YASoYyWCGbLD4YAvBDNll8MARQMpTBCtlk8cEQgB+yyeKDIYCSoQxWyCaLD4YA/JBNFh8MAZQMZbBCNll8MATgh2yy+GAIoGQogxWyyeKDIQA/ZJPFB0MAJUMZrJBNFh8MAfghmyw+GAIoGcpghWyy+GAIwA/ZZPHBEEDJUAYrZJPFB0MAfsgmiw+GAEqGMlghmyw+GALwQzZZfDAEUDKUwQrZZPHBEIAfssnigyGAkqEMVsgmiw+GAPyQTRYfDAGUDGWwQjZZfDAE4IdssvhgCKBkKIMVssnigyEAP2STxQdDACVDGayQTRYfDAH4IZssPhgCKBnKYIVssvhgCMAP2WTxwRBAyVAGK2STxQdDAH7IJosPhgBKhjJYIZssPhgC8EM2WXwwBFAylMEK2WTxwRCAH7LJ4oMhgJKhDFbIJosPhgD8kE0WHwwBlAxlsEI2WXwwBOCHbLL4YAigZCiDFbLJ4oMhAD9kk8UHQwAlQxmskE0WHwwB+CGbLD4YAigZymCFbLL4YAjAD9lk8cEQQMlQBitkk8UHQwB+yCaLD4YASoYyWCGbLD4YAvBDNll8MARQMpTBCtlk8cEQgB+yyeKDIYCSoQxWyCaLD4YA/JBNFh8MAZQMZbBCNll8MATgh2yy+GAIoGQogxWyyeKDIQA/ZJPFB0MAJUMZrJBNFh8MAfghmyw+GAIoGcpghWyy+GAIwA/ZZPHBEEDJUAYrZJPFB0MAfsgmiw+GAEqGMlghmyw+GALwQzZZfDAEUDKUwQrZZPHBEIAfssnigyGAkqEMVsgmiw+GAPyQTRYfDAGUDGWwQjZZfDAE4IdssvhgCKBkKIMVssnigyEAP2STxQdDACVDGayQTRYfDAH4IZssPhgCKBnKYIVssvhgCMAP2WTxwRBAyVCGWvSCCSXrER8MAfghmyw+GAIoGcpQCwTgBgLwQzZZfDAEUDKUoRYIwA0E4IdssvhgCKBkKEMtEIAbCMAP2WTxwRBAyVCGWiAANxCAH7LJ4oMhgJKhDLVAAG4gAD9kk8UHQwAlQxlqgQDcQAB+yCaLD4YASoYy1AIBuIEA/JBNFh8MAZQMZagFAnADAfghmyw+GAIoGcpQCwTgBgLwQzZZfDAEUDKUoRYIwA0E4IdssvhgCKBkKEMtEIAbCMAP2WTxwRBAyVCGWiAANxCAH7LJ4oMhgJKhDLVAAG4gAD9kk8UHQwAlQxlqgQDcQAB+yCaLD4YASoYy1AIBuIEA/JBNFh8MAZQMZagFAnADAfghmyw+GAIoGcpQCwTgBgLwQzZZfDAEUDKUoRYIwA0E4IdssvhgCKBkKEMtEIAbCMAP2WTxwRBAyVCGWiAANxCAH7LJ4oMhgJKhDLVAAG4gAD9kk8UHQwAlQxlqgQDcQAB+yCaLD4YASoYy1AIBuIEA/JBNFh8MAZQMZagFAnADAfghmyw+GAIoGcpQCwTgBgLwQzZZfDAEUDKUoRYIwA0E4IdssvhgCKBkKEMtEIAbCMAP2WTxwRBAyVCGWiAANxCAH7LJ4oMhgJKhDLVAAG4gAD9kk8UHQwAlQxlqgQDcQAB+yCaLD4YASoYy1AIBuIEA/JBNFh8MAZQMZagFAnADAfghmyw+GAIoGcpQCwTgBgLwQzZZfDAEUDKUoRYIwA0E4IdssvhgCKBkKEMtEIAbCMAP2WTxwRBAyVCGWiAANxCAH7LJ4oMhgJKhDLVAAG4gAD9kk8UHQwAlQxlqgQDcQAB+yCaLD4YASoYy1AIBuIEA/JBNFh+shgA+vGya5unbPX+5efPZT4sfL5oFT3ZW4R4v+UAQwNiJD4YA/JBNFh+sggCu5o396PXuny6alQDOEYADCMANBOCHbLL4YA8XwPVJ8/ht+fCmOfqu/6fZp/6lAD4+3/3zchUOfskHgwDGTnwwBOCHbLL4YA8XwHnz+H1pt/b0P9vffN+sBXB90j1r7yoc/JIPBgGMnfhgCMAP2WTxwR4sgI/Pm/m2n6v15p6On180zenqsavdTT+rVTj0JR8OAhg78cEQgB+yyeKDPVgAq76/MkFZ/nr0ai2Fi2bPLoLFKhz6kg8HAYyd+GAIwA/ZZPHBHiyAi2axbefmTfNs4/GPz796v/Gt4Pyzf/nmpGme/rhnFQ59yYeDAMZOfDAE4IdssvhgDxbA+YYAtrby/Fq2vx4seLazBARQDwTgBgLwQzZZfLAKAniy89OKlQCuT5qjr9+X377fYwAEUA8E4AYC8EM2WXwwJwFcNYt9AOfrXcX/uuTf3Tk+9n9NF2SD5WNHANErBHA4TgJY8fH5zlcAvgHUg28AbvANwA/ZZPHBvAWwe7oAAqgIAnADAfghmyw+mN1O4JZdAezRBAKoBwJwI4EAZIdxH9lk8cHMDgPtQADOIAA3EIAfssnig5mdCLb9xxV7NMHMqQcCcAMB+CGbLD6Y3aUgth5bbSi6PtnRBDOnHgjADQTgh2yy+GCGF4MrW4eBzi8GOnvWzkXhmDn1QABuIAA/ZJPFBzO9HPRaALPGf/SqlA8vmt3tRMyceiAANxCAH7LJ4oNVuCHMu60bwlxttvj1ZqG29XfPerW7Coe/5EORnTkIwA0E4Idssvhg1W8JeYsAys0PX8za/5d7bhzJzKkHAnADAfghmyw+GDeFLxnKUAsE4AYC8EM2WXwwBFAylKEWCMANBOCHbLL4YAigZChDLRCAGwjAD9lk8cEQQMlQhlogADcQgB+yyeKDIYCSoQy1QABuIAA/ZJPFB0MAJUMZaoEA3EAAfsgmiw+GAEqGMtQCAbiBAPyQTRYfDAGUDGWoBQJwAwH4IZssPhgCKBnKUAsE4AYC8EM2WXwwBFAylKEWCMANBOCHbLL4YAigZChDLRCAGwjAD9lk8cEQQMlQhlogADcQgB+yyeKDIYCSoQy1QABuIAA/ZJPFB0MAJUMZaoEA3EAAfsgmiw+GAEqGMtQCAbiBAPyQTRYfDAGUDGWoBQJwAwH4IZssPhgCKBnKUAsE4AYC8EM2WXwwBFAylKEWugJIVzIE4IdssvhgCKBkKEMtEIAbCMAP2WTxwRBAyVCGWiAANxCAH7LJ4oMhgJKhDLVAAG4gAD9kk8UHQwAlQxlqgQDcQAB+yCaLD4YASoYy1AIBuIEA/JBNFh8MAZQMZagFAnADAfghmyw+GAIoGcpQCwTgBgLwQzZZfDAEUDKUoRYIwA0E4IdssvhgCKBkKEMtEIAbCMAP2WTxwRBAyVCGWiAANxCAH7LJ4oMhgJKhDLVAAG4gAD9kk8UHQwAlQxlqgQDcQAB+yCaLD4YASoYy1AIBuIEA/JBNFh8MAZQMZagFAnADAfghmyw+GAIoGcpQCwTgBgLwQzZZfDAEUDKUoRYIwA0E4IdssvhgCKBkKEMtEIAbCMAP2WTxwRBAyVCGWiAANxCAH7LJ4oMhgJKhDLVAAG4gAD9kk8UHQwAlQxlqgQDcQAB+yCaLD4YASoYy1AIBuIEA/JBNFh8MAZQMZajE8bFqsnwlQwB+yCaLD4YASoYyVOF4yfohkWQlYckQgB+yyeKDIYCSoQw1OD7eNYBGspZ0JUMAfsgmiw+GAEqGMlTg+HiPASSSdaQrGQLwQzZZfDAEUDKU4eEcH+8zgEKyOelKhgD8kE0WHwwBlAxleDgIwBkE4IdssvhgCKBkKMODOe4zf1gg2YJ0JUMAfsgmiw+GAEqGMjwYBOANAvBDNll8MARQMpThwSAAbxCAH7LJ4oMhgJKhDA8GAXiDAPyQTRYfDAGUDGV4OOwEdgYB+CGbLD4YAigZyvBwEIAzCMAP2WTxwRBAyVCGh4MAnEEAfsgmiw+GAEqGMlRgX//XSNaRrmQIwA/ZZPHBEEDJUIYa7On/Isla0pUMAfghmyw+GAIoGcpQhd3+r5KsJCwZArBD97YWPeKDIYCSoQyV2O7+RShZvpIhADsQgBsIoGQoQy10Z066kiEAO3SHcY/4YAigZChDLXRnTrqSIQA7dIdxj/hgCKBkKEMtdGdOupIhADt0h3GP+GAIoGQoQy10Z066kiEAO3SHcY/4YAigZChDLXRnTrqSIQA7dIdxj/hgCKBkKEMtdGdOupIhADt0h3GP+GAIoGQoQy10Z066kiEAO3SHcY/4YAigZChDLXRnTrqSIQA7dIdxj/hgkxRA/3yp+DLUQnfmpGt2CMAO3WHcIz7YBAUwoSvm6CaLD4YA7NAdxj3ig01PAFO6ZqZusvhgCMAO3WHcIz7Y5AQwqavm6yaLD4YA7NAdxj3ig01NANO6c65usvhgCMAO3WHcIz4YAigZylAL3ZmTrtkhADt0h3GP+GATE8Bxn+7R+DLUQnfmpGt2CMAO3WHcIz7YMAH8aroKlgvvgQDGSrpmhwDs0B3GPeKDDRLAx+ePvn5vtwpmS94FAYyVdM0OAdihO4x7xAcbKICmaZ5+a7UKRsvdBwIYK+maHQKwQ3cY94gPNlwAM758a7IKFgu9DXYCj5R0zQ4B2KE7jHvEBxskgJsfTuYGaI6+rr87AAHUQ3fmpGt2CMAO3WHcIz7Y0KOAfls54NGryrsDEEA9dGdOumaHAOzQHcY94oMdcBjobz98sXBA3d0BnAlcD92Zk67ZIQA7dIdxj/hgh50HcLN0wOffVVyFeosawm7/T1CGWujOnHTNDgHYoTuMe8QHO0gAq/7fNEf1DMDVQOuhO3PSNTsEYIfuMO4RH2y4ANbd/6jdHfCk3ipUW9JQtrp/KWdn7mtghe7MSdfsEIAdusO4R3ywgQJYb/8/+vJtuTlvms9+qrYKtRY0nO0BhgBGQLpmhwDs0B3GPeKDHXQeQNM8/bF74PoEAaREd+aka3YIwA7dYdwjPtghAvj82/frBxTvbskAACAASURBVBBARnRnTrpmhwDs0B3GPeKDDRbA1tWAPr78Xb2LA4XPHAQwAtI1OwRgh+4w7hEfbJgA/pPBCcDrVbBb9G0ggNGRrtkhADt0h3GP+GDDLgXxX//pj8sP/Ncvv/iq8irUXdwQEMDoSNfsEIAdusO4R3ywoZuAVpv8Zz8/rnstiPCZgwBGQLpmhwDs0B3GPeKDHSyAqgcAzVeh6tIGgQBGR7pmhwDs0B3GPeKD3SmA82YHBJAW3ZmTrtkhADt0h3GP+GB3CuD6ZEcAbAJKi+7MSdfsEIAdusO4R3ywuzcB7X4FeF15FeoubggIYHSka3YIwA7dYdwjPtjdArh+edpeBuLR6Zynf6x9W7DwmYMARkC6ZocA7NAdxj3igx28E9hgFcyWfCsIYHSka3YIwA7dYdwjPtjB5wEYrILZkm8FAYyOdM0OAdihO4x7xAc77IYwNqvg/5IIYHSka3YIwA7dYdwjPhgCQACjIF2zQwB26A7jHvHB7hLAx5enT3+a/WeTp5wHkBXdmZOu2SEAO3SHcY/4YHcKoN3/u74fACeC5UZ35qRrdgjADt1h3CM+GAJAAKMgXbNDAHboDuMe8cEQAAIYBemaHQKwg2HsBjuBEcAoSD9zwodxgvekFgxjNxAAAhgF6WdO+DBO8J7UgmHsBgJAAKMg/cwJH8YJ3pNaMIzdQAAIYBSknznhwzjBe1ILhrEbA84D2IHzANLCzHEDAdjBMHZjwFFA3BBmPDBz3EAAdjCM3UAACGAUpJ854cM4wXtSC4axGwgAAYyC9DMnfBgneE9qwTB2g53ACGAUpJ854cM4wXtSC4axGwgAAYyC9DMnfBgneE9qwTB2AwEggFGQfuaED+ME70ktGMZuIAAEMArSz5zwYZzgPakFw9gN7gegLADdZPlmTvgwTvCe1AIBuMHVQJXbpG6yfDMnfBgneE9qgQDcQADKbVI3Wb6ZEz6ME7wntUAAbiAA5TapmyzfzAkfxgnek1ogADfYCazcJnWT5Zs54cM4wXtSCwTgBgJQbpO6yfLNnPBhnOA9qQUCcAMBKLdJ3WT5Zk74ME7wntQCAbgxXAC//XXGv703WIX6i7wLBDA60s+c8GGc4D2pBQJwY6gAfn6x2AP8+dvqq1B7gXeDAMbG8fF2tHwzJ3wYJ3hPaoEA3BgogIuNg4Be1V6FyssbAAIYF8dLVo/kmznhwzjBe1ILBODGMAFcbR4FevRd5VWou7ghIIBRcXy8Y4B8Myd8GCd4T2qBANwYJoDzWd9/9PWP//jlL+2WoCeVV6Hu4oaAAMbE8fGuAfLNnPBhnOA9qQUCcGOQANoTwZZd/5wTwRKjKIDj4z0GyDdzwodxgvekFgjAjaECWG336U4NrrsKVZc2CAQwIvYJIEEwBGAHAnBjkABu3qyb/kwAj+seCxo+cxJ0k1oICuC4T/tggmAIwA4E4MawfQAXTfN68eMV+wASgwDcQAB2IAA3hglg9hVgsQ3o3UntLUDxMydBN6kFAnADAdiBANwYcEOYli/awz+f/uG0/Z+vK58OHD5zEnSTWiAANxCAHQjAjQGXg96BncBpERQAO4FvJV03qQUCcAMBpOgmtUAAbiAAOxCAGwggRTepBQJwAwHYgQDc4HLQKbpJLRQFsO9M4ATBEIAdCMANBJCim9RCUgB7rgWUIBgCsAMBuIEAUnSTWmgKYPdqoAmCIQA7EIAbhwvg5uf/g30AWREVwM79ABIEQwB2IAA3Bgrgw0t2Ao8BWQHkKxkCsAMBuDFMANvHAiGAtCAANxCAHQjAjcHXAkIAYwABuIEAzOht7xNKlq9kQ68G2jSPvpj9/ym3hEwNAnADARixs8dfJllJWLLBN4SZtf3zo++2rgxdaxXqLm4I6bpJLRCAGwjAht1jflWStaQr2VABtF3/onlWyvUJl4POCwJwAwGYsOesP5FkHelKdogArtrWzw1hMoMA3EAAFuy77odGsjnpSjZ4H0D34X+mgSt2AicGAbiBACxAAM4MPwros59mHnj0hxMEkBgE4AYCMOC4z/xhgWQL0pVs+HkAs7a/OBqUfQBpQQBuIAADEIA3A88EvvmmFcD8fLDFzSHrrULdxQ0hXTepBQJwAwEYgAC8GXwtoN/+n/elfHjRNJ9X7v/xMydBN6kFAnADARiAALw5+GJwdY8A6lah+hLvJF03qQUCcAMBWMBOYGe4HHSKblILBOAGArAAATgzXAC//XXGv9X/AhA/cxJ0k1ogADcQgAUIwJmhAvj5xeJKcJ+/rb4KtRd4N+m6iRFCydKVDAGYsK//ayTrSFeygQLYvBwoF4MbDULJ0pUMAdiwp/+LJGtJV7JhArjavBo0h4GOBqFk6UqGAIzY7f8qyUrCkg0TwHl7Oeivf/zHL395wYlgI0IoWbqSIQAztrt/EUqWr2SDLwe97PrnXApiPAglS1cyBGAHdwRzY6gAVtt9FlcGrbkKVZc2iHTdxAihZOlKhgDsQABuDL0a6KrpcznoESGULF3JEIAdCMCNwVcDfb348Yp9AONBKFm6kiEAOxCAG8MEMPsKsNgG9O6EW0KOB6Fk6UqGAOxAAG7cJYCPL09bvmgP/3z6h/ae8EdfVz4dOHzmJOgmRgglS1cyBGAHAnDjTgE8b3ZgJ/BYEEqWrmQIwA4E4AYCSNFNjBBKlq5kCMAOBOAGAkjRTYwQSpauZAjADgTgBpeDTtFNjBBKlq5kCMAOBOAGAkjRTYwQSpauZAjADgTgBgJI0U2MEEqWrmQIwA4E4MYBAvjlr3/9648Wq2CwzDtI102MEEqWrmTxAuhfMy2+m9QCAbgxVAA3P5zMrwX9X+qvQvUl3km6bmKEULJ0JYsWwISumq+bLD7YQAHcvFkdA1T5SkAIwBChZOlKFiyAKd03SzdZfLCBAjjfOAq08qWAEIAdQsnSlSxWAJO6c65usvhgw+8IdvT1j//4+z+3G4Je733O/Veh7uKGkK6bGCGULF3JQgWw997p8d2kFgjAjcF3BFts+Wm3BXE10LEglCxdyRCAHQjAjaH3A1jdEOb6RO1+AGdnCdqJDULBEMAGx326R+O7SS0QgBtD7wi2eUMYqUtBnC3xXwt7hFIhgA0QwFhBAHtWoerSBrEuw9mZsgGEQiGADRDAWBmnANoN/88WP19UPxA0UgBnZ9IGEMqEADZAAGNlnAJou37z+7bt3/y5Wbug1irUXdwQlmU4O9M2gFAkBLAJO4FHykgFML8o9KPuxmBKt4REAKMBAWyCAEbKSAXQnQiwpPJpAIECOOvjvya2CCVCAJsggJEyVgG0N4Ofc/Sq+irUXuDdIIDRgQC22NP/E3STWiAANwZfDfTmh3b7z+nXta8EhAAMEUqEALbZ7f8JukktEIAbk74fAAIYDwigx07/T9BNaoEA3BgmgL8ZfPBfr4Ldom+DncCjAwHssNX9S4ZuUgsE4MbQ8wCqXwR6YxXMlnwrCGB0IIBd0nWTWiAANw4+E9hgFcyWfCsTEYBSJgSwS7puUgsE4MbBF4Pbw4eXTdM8fbv3H67FceuzOBPYBq1cCGCXdN2kFgjAjcH3A/h8X4Nf/rE7PnTP+QEX628Otz+LawGZIJYMAeySrpvUAgG4MewbwN+/mZ8JPOfp1uag9vrQb8uHfV8SLtanDX/iWVwN1AK17zYIYJd03aQWCMCNofsANtneH3A+30F886Z/o5ib7zeee+uz4meOSIfcRm7vBgLYJV03qQUCcOPBApj9bb5V56onhp9fNM3p8rFbn1USzJwE3aQ6CMAaBGAHAnDjwQK42unxq39z9Gr1x9ue1a3CQ9b/fqTrJrU56xO9Qg8mXckQgB2yAsh36saDzwS+2Lhb8OZ1oj8+/+r9uu/f9qxuFQ59yYeTrpvUBgGYgwDsEBVAxpO3HyyA843WvrV5/9ey8cH/1meVBDMnQTepDQIwBwHYoSmAlJdvqiCAJzs/rdgQwO3PCp85CbpJbRCAOQjADkkB5LyA63AB3Pz9r3/967/tXBHi/gL41yX/7s7x8eZvZ2f+a2BNr/9Hr87DSVeyHQH4r8L2eyKEYrDeLRyiV2fJUAF8eLHYA9w/IYxvAClR+wKQr2R8A7BD8RtA0nv4DBTAu/UxQL1zeRFAShCANQjADkEBHPfpHo0PNkwA1ycbR4Fun8v7qd277ASOQ6z/5ysZArADAbgxTADn7YUgvv7xH7/8pd0StNXBP3WAJ4eBBqLV//OVDAHYgQDcGHwi2JPF7t/zgSeCbf+RE8H8ker/+UqGAOxAAG4MFcBqu8/1ycBLQWw9xqUgIpDp/iVhyRCAHYICGPVO4Js367Y9a+Xbdwf7xGXeNtp94ovBZegmRgglS1cyBGAHAnBj8D6A5Yabq6a3Ef8TF3reEEDay0GXFN3ECKFk6UqGAOxAAG4MPgro6Nvup3cnOx/h320dHnrVbGzk39jg827vQaTdKtxrxR9Eum5ihFCydCVDAHYoCmDMZwLf/P2Hk1nvPv3D6fq+MBt3hdm62eNtAsh5S8iOBN3ECKFk6UqGAOyQFMCIrwXUuxz0nrvCPGgVai1oOOm6iRFCydLdwwcB2KEpgPFeDRQBjBShZOnu4okA7BAVwGjvB4AARopQsnXJkpzhhgDskBVAvpI9+HLQFVbB/yURwOhYlSzLNS4QgB0IwA0EINUmewglW5YszVXuEIAdCMANBCDVJnsIJUMAu6TrJrVAAG4gAKk22UMo2aJkZ33CVggB2IEA3EAAUm2yh1AyBLBLum5SCwTgBgKQapM9hJIhgF3SdZNaIAA3EIBUm+whlAwB7JKum9QCAbiBAKTaZA+hZOwE3iVdN6kFAnADAUi1yR5CyRDALum6SS0QgBsIQKpN9hBKhgB2SddNaoEA3EAAUm2yh1AyzgTeJV03qQUCcAMBSLXJHkLJuBbQLum6SS0QgBt3CeDjy9Mdnla7Dtx8FaoubRAIYHRwNdBd0nWTWiAAN+4UgOmFQOerUHVpg0AAo4P7AeySrpvUAgG4gQCk2mQPoWTpSoYA7EAAbhwugKPf/fF93VWourRBpOsmRgglS1cyBGAHAnBj4E7gi6b5/a+z/735vmke1+3/8TMnQTcxQihZupIhADsQgBvDBHDVNM8WP16sf6y1CnUXN4R03cQIoWTpSoYA7EAAbgwSwM2b9Xb/j89rfwUInzkJuokRQsnSlQwB2IEA3Bh6T+BNAbATeCwIJUtXMgRgBwJwY/BN4V8vfr7iKKDxIJQsXckQgB0IwI2hm4Cao1fdj+9Oqu8FDp85CbqJEULJ0pUMAdiBANwYthP4ojsA9PT0tPtfdgKPBaFk6UqGAOxAAG4ME0D7FWAFh4GOBqFk6UqGAOxAAG4MPA9gwwBfVe7/8TMnQTcxQihZupIhADsQgBuDrwb6yzdfzLr/oy/f1l+F6ku8k3TdxAihZOlKhgDsQABucDnoFN3ECKFk6UqGAOxAAG4ggBTdxAihZOlKhgDsQABuDBbAzV9enrRnAHz8b9VXofYC7yZdNzFCKFm6kiEAOxCAG0MF8LeTxYWgr2ofBRo/cxJ0EyOEkqUrGQKwAwG4MfxqoAsBnDdH31VehbqLG0K6bmKEbLIEwRCAHzrJ0pVs8NVAm+aLVgDt8aBPKq9C3cUNAQGMnATBEIAfOsnSlWyYAM6b5vHb+WXgLrgUxHiQTZYgGALwQydZupINvRbQ0XeL64ByNdARIZssQTAE4IdOsnSd54DLQSOA0SGbLEEwBOCHTrJ0nWeoAFbfAK5PEMBokE2WIBgC8EMnWbrOM/hy0I/fdwJgJ/CYkE2WIBgC8EMnWbrOc8BRQKcnzdF/bH94vfc591+FuosbQroyGCGbLEEwBOCHTrJ0nWfgeQDnXA56jMgmSxAMAfihkyxd5xl6Oejvzfp//MxJUAYjZJMlCIYA/NBJlq7zDL8c9Muu/T/9tv4qVF/inaQrgxGyyRIEQwB+6CRL13kOuhroP2xWwWSpnyRdGYyQTZYgGALwQydZus4z7Cig//pPf1xu+Ll++cVXlVeh7uKGkK4MRsgmSxAMAfihkyxd5zngRLDVz1wKYizIJksQDAH4oZMsXec5WACcCDYiZJMlCIYA/NBJlq7z3CmAzQNAm9VFoauuQtWlDSJdGYyQTZYgGALwQydZus5zpwCuT3YEwCagsSCbLEEwBOCHTrJ0nefuTUC7XwE4E3gsyCZLEAwB+KGTLF3nuVsA1y9Pv5h1/Uenc57+8W3tVai8vAGkK4MRsskSBEMAfugkS9d5Dt4JbLAKZku+lXRlMEI2WYJgCMAPnWTpOs/B5wEYrILZkm8lXRmMkE2WIBgC8EMnWbrOc9CZwEar4P+S6cpghGyyBMEQgB86ydJ1nqECeHf6Xfe/1ydfcTG40SCbLEEwBOCHTrJ0nWegAC6a5tnyh8r3g4mfOQnKYIRssgTBEIAfOsnSdZ5hAmhPBuj6fntDMA4DHQ2yyRIEQwB+6CRL13mGCeB8efpXd18Abgk5FmSTJQiGANxIUO1apOs8g+8J/Gzx8xVnAo8H2WQJgiEANxJUuxbpOs99rgbKtYDGgmyyBMEQgBsJql2LdJ0HAaQogxGyyRIEQwBuJKh2LdJ1nsGbgJZ7ftkENCJkkyUIhgDcSFDtWqTrPIN3Ah/NzwP4+YSdwONBNlmCYAjAjQTVrkW6zjNMAFftRUCPTk9PuRroqJBNliAYAnAjQbVrka7zDDwRbPOa0JwINhpkkyUIhgDcSFDtWqTrPAMF0B3/P6fyLeETzJwEZTBCNlmCYAjAjQTVrkW6zjP4YnAfvunuCvBl7bsBJJg5CcpghGyyBMEQgBsJql2LdJ2Hq4GmKIMRsskSBEMAbiSodi3SdR4EkKIMVqj2hAQlQwBuJKh2LdJ1HgSQogxWqPaEBCVDAG4kqHYt0nWeuwTw8eXp059m/9nkKWcCjwXVnpCgZAjAjQTVrkW6znOnANorP8z+swmXghgNqj0hQckQgBsJql2LdJ0HAaQogxWqPSFByRCAGwmqXYt0nQcBpCiDFao9IUHJ0gkgwXtihFCydCWb5k7gbRKUwQoEYAYCcEMoWbqSIYAUZbACAZiBANwQSpauZAggRRmsQABmIAA3hJKlKxkCKLptUjdZwpmDAMwQSpauZAPOA9hh/OcB9FBtk7rJEs4cBGCGULJ0JRtwFNAO4z8KqIdqm9RNlnDmIAAzhJKlKxkCKLptUjdZwpmDAMwQSpauZIcL4Oh3fxz9PYF7qLZJ3WQJZw4CMEMoWbqSDdwJfNE0v/+1zG8MU/me8AjAENVkCWcOAjBDKFm6kg2+J/CzxY8X6x9rrULdxd0D1TapmyzhzEEAZgglS1eyQQK4ebPe7v/xee2vAAjADtVkCWcOAjBDKFm6kg0SQHdBoD0/V1qFqku7D6ptUjdZwpmDAMwQSpauZEMF0Lxe/HzFUUAjQjVZwpmDAMwQSpauZEM3ATVHr7of351U3wuMAOxQTZZw5iAAM4SSpSvZsJ3AF90BoKenp93/shN4NKgmSzhzEIAZQsnSlWyYANqvACs4DHQ8qCZLOHMQgBlCydKVbOB5ABsG+Kpy/0cAhqgmSzhzEIAZQsnSlWzw1UB/+eaLWfd/9OXb+qtQfYmHotomdZMlnDkIwAyhZOlKxuWgi26b1E2WcOYgADOEkqUrGQIoum1SN1nCmYMAzJBNliDYYAHc/OXlSXsGwMf/Vn0Vai/wYFTbpG6yhDMHAZghmyxBsKEC+NvJ4kLQV7WPAkUAhqgmSzhzEIAZsskSBBt+NdCFAM6bo+8qr0Ldxd0D1TapmyzhzEEAZsgmSxBs8NVAm+aLVgDt8aBPKq9C3cXdA9U2KZwsPhgCcEM2WYJgwwRw3jSP384vA3fBpSBGhGyy+GAIwA3ZZAmCDb0W0NF3i+uAcjXQMSGbLD4YAnBDNlmCYAdcDhoBjA/ZZPHBEIAbsskSBBsqgNU3gOsTBDAeZJPFB0MAbsgmSxBs8OWgH7/vBMBO4FEhmyw+GAJwQzZZgmAHHAV0etIc/cf2h9d7n3P/Vai7uHsQ302skE0WHwwBuKGa7OwsPtnA8wDOuRz0KJFNFh8MAbihmexsSehaDL0c9Pdm/R8BGCKbLD4YAnBDMtnZWQoDDL8c9Muu/T/9tv4qVF/iocR3Eytkk8UHQwBuKCY7O8thgIOuBvoPm1UwWeohxHcTK2STxQdDAG4IJjs7S2KAYQL429e1t/tsroLdogcS302skE0WHwwBuCGYbFwCuHlTf8v/xiqYLXko8d3ECtlk8cEQgBt6yc76hK3JAWcCm62C2ZKHEt9NrJBNFh8MAbihl2xkAphfC8hsFcyWPJT4bmKFbLL4YAjADb1kIxNAeyLY5/XvBr9cBasFDya+m1ghmyw+GALwIvpISQNGJoCbv3/THgL66HTOU64FNBZkk8UHQwA+xPdIC7L0/8H7ADbhYnCjQTZZfDAE4EKKLlkfBLCxClWXdh/iu4kVssnigyEAD5K0yeoggI1VqLq0+xDfTayQTRYfDAE4kKZPVidLroPOBDZaBf+X7BHfTayQTRYfDAE4oCuALNu2EEDJ0E2skE0WHwwB2JPnYBkDcoRCACVDN7FCNll8MARgj7QAchzfOkAANz+/PD39Xf2rgK5WwWzJQ4nvJlbIJosPhgDsERdAhpLdLYAPL4zuA7BaBaPlDie+m1ghmyw+GAKwBwGYc6cA1kcAWRkAAdghmyw+GAJwQLv/ZyjZnQK4WB/+WflewKtVsFnsAcR3Eytkk8UHQwAOIABr7hLAzZv2wP8f/2f7PeCJ0SrYLPYA4ruJFbLJ4oMhAAcQgDWDBPC6uxyc1TYgBGCHbLL4YAjAA+n+n6Fkdwmg3QXQXgr6+qT6GcCrVTBZ6iHEdxMrZJPFB0MALij3/wwlGyKAtvEv/9diFUyWegjx3cQK2WTxwRCAD8L9P0PJEEDJ0E2skE0WHwwBeCHa/UuKkiGAkqGbWCGbLD4YAnBDNlmCYAigZOgmVsgmiw+GANyQTZYg2BABtLcC+2LjlmDcEWw0yCaLD4YA3JBNliDYIAH04H4Ao0E2WXwwBOCGbLIEwRBAydBNrJBNFh8MAbghmyxBMARQMnQTK2STxQdDAG7IJksQjPsBlAzdxArZZPHBEIAbsskSBEMAJUM3sUI2WXwwBOCGbLIEwRBAydBNrJBNFh8MAbghmyxBMARQMnQTK2STxQdDAG7IJksQDAGUDN3ECtlk8cEQgBuyyRIEQwAlQzexQjZZfDAE4IZssgTBEEDJ0E2skE0WHyybAHQvmZahT9qQIBgCKBm6iRWyyeKD5RKA8kWTM/RJGxIEQwAlQzexQjZZfLBUApC+bUqGPmlDgmAIoGToJlbIJosPlkkA2jdOzNAnbUgQDAGUDN3ECtlk8cESCUD81ukZ+qQNCYIhgJKhm1ghmyw+GAJwQzDSnATBEEDJ0E2skE0WHyyPAM76+K+JLXqJFiQIhgBKhm5ihWyy+GAIwA29RAsSBEMAJUM3sUI2WXwwBOCGXqIFCYIhgJKhm1ghmyw+GAJwQy/RggTBEEDJ0E2skE0WHyyPANgJPFYSBEMAJUM3sUI2WXywBAJYggBGSoJgCKBk6CZWyCaLD4YA3BCMNCdBMARQMnQTK2STxQdLJADOBB4pCYIhgJKhm1ghmyw+WCYBcC2gcZIgGAIoGbqJFbLJ4oOlEgBXAx0lCYIhgJKhm1ghmyw+WC4BcD+AMZIgGAIoGbqJFbLJtoNdXvqvQTYBZOgmRsgmSxAMARThNqmbDAHskKCbGCGbLEEwBFCE26RuMgSwQ4JuYoRssgTBEEARbpO6yRDADgm6iRGyyRIEQwBFuE3qJkMAOyToJkbIJksQDAEU4TapmwwB7JCgmxghmyxBMARQhNukbjIEsEOCbmKEbLIEwRBAEW6TuskQwA4JuokRsskSBEMARbhN6iZDADsk6CZGyCZLEAwBFOE2qZsMAeyQoJsYIZssQTAEUITbpG4yBLBDgm5ihGyyBMEQQBFuk7rJEMAOCbqJEbLJEgRDAEW4TeomQwA7JOgmRsgmSxAMARThNqmbDAHskKCbGCGbLEEwBFCE26RuMgSwQ4JuYoRssgTBEEARbpO6yRDADgm6iRGyyRIEQwAlQzexAgGYgQDckE2WIBgCKBm6iRUIwAwE4IZssgTBEEDJ0E2sQABmIAA3ZJMlCIYASoZuYgUCMAMBuCGbLEEwBFAydBMrEIAZCMAN2WQJgiGAkqGbWIEAzEAAbsgmSxAMAZQM3cSKrWSywRBAS4JuYoRssgTBEEDJ0E2sQABmIAA3ZJMlCIYASoZuYgUCMAMB+KG6JTNByRBAydBNrEAAZiAAPxCAGQigZOgmViAAMxCAHwjADARQMnQTKxCAGQjADwRgBgIoGbqJFQjADATgBwIwo4YAPrxsmubp208/fNEseLKzCvd4ybrEdxMrEIAZCMAPVQEkCFZBAFfzxn70+pMPnyOAABCAGQjAj/g+aUR8sIcL4Pqkefy2fHjTHH33iYc/Pt/+8+YqHPyStYnvJlYgADMQgB/xfdKI+GAPF8B58/j97H9u3mx/tu89fH3S/bp3FQ5+ydrEdxMrEIAZCMCP+D5pRHywBwvg4/NmvpHnqvnsp9sfvtrd9LNahUNfsjrx3cQKBGAGAvAjvk8aER/swQJY9f1Vy9/78EXzes8/nq/CoS9ZnfhuYgUCMAMB+BHfJ42ID/ZgAVw0i207N2+aZ7c/fP7Zv3xz0jRPf9yzCoe+ZHXiu4kVCMAMBOBHfJ80Ij7YgwVwvtHpn9z68Ox7wIJnO0sInzkJuokVCMAMBOBHfJ80Ij5YBQE82flp9+Hrk+bo6/flt+/3GCB85iToJlYgADMQgB/xfdKI+GBOArhqFvsAzte7iv91yb/n4vIyeg2MkA0WkmxHAP6rsM3ZWfQa1hWk0QAAFGtJREFUwPhwEsCKj893vgKEf3RK8HHSCr4BmME3AD/iPygbER/MWwD90wVKgpmToJtYgQDMQAB+xPdJI+KDOe0E3vwDAvADAZiBAPyI75NGxAfzOgx0BQLwBAGYgQD8iO+TRsQH8zoRbEnfByXBzEnQTaxAAGYgAD/i+6QR8cGcLgWx2iJ0fbJzSnD4zEnQTaxAAGYgAD/i+6QR8cGcLgZ3tbgo6OzXnYvChc+cBN3ECgRgBgLwI75PGhEfzOly0LPGf/SqlA8vmt1rAoXPnATdxIppCODyMiAaAvAjvk8aER+swg1h3m3d+WV1ytf2w23r7359tbsKh79kZRDA6NgIdrnEdw0QgB/xfdKI+GDVbwm5EkDvlpA3P3wxa/9f7tw4MsHMQQDjYx3s8jLGAAjAj/g+aUR8MG4KXxDACFkFu7wMMgAC8CO+TxoRHwwBFAQwQpbBLi+jDIAA/Ijvk0bEB0MABQGMEASwAwIYH/HBEEBBACNkEeyyj98aIAA/4vukEfHBEEBBACMEAeyAAMZHfDAEUBDACEEAOyCA8REfDAEUBDBCEMAOCGB8xAdDAAUBjBB2Au+AAMZHfDAEUBDACEEAOyCA8REfDAEUBDBCEMAOCGB8xAdDAGUqAvA+VdYUzgTeAQGMj/hgCKBMQwAhTdIOrgW0AwIYH/HBEECZhACi2qQVXA10BwQwPuKDIYAyBQGEbSixgvsB7IAAxkd8MARQJiCAuF2lVsSXDAH4Ed8njYgPhgBKhm5iBQIwAwH4Ed8njYgPhgBKhm5ixTzZZZ/gtapAfMkQgB/xfdKI+GAIoGToJlYgADPSCSBBN7FCNll8MARQMnQTKxCAGQjAD9lk8cEQQMnQTaxAAGYgAD9kk8UHQwAlwzGFVrAT2AwE4IdssvhgCKBkOKvICgRgBgLwQzZZfDAEUDJcV8AKBGAGAvBDNll8MARQMlxZzIppnAmMADriu4kVssnigyGAkuHawlZM4VpABQHMie8mVsgmiw+GAMokBCC6c2MBAmiJ7yZWyCaLD4YASoYbzFqhfz+ADgTQEt9NrJBNFh8MAZSpCEDwBIclCKAlvptYIZssPhgCKAhghCCAXeK7iRWyyeKDIYCCAEYIAtglvptYIZssPhgCKNPYCVwQQF0QgB+yyeKDIYCCAEYIAtglvptYIZssPhgCKAhghCCAXeK7iRWyyeKDIYAyhTOBOzQizUEAu8R3Eytkk8UHQwBlAtcCmiOSqQMB7BLfTayQTRYfDAGUCVwNdI5KqBYEsEt8N7FCNll8MARQJnA/gDlCuRDAHuK7iRWyyeKDIYCSoZtYgQDMQAB+yCaLD4YASoZuYgUCMAMB+CGbLD4YAigZuokVCMAMBOCHbLL4YAigZOgmViAAMxCAH7LJ4oMhgJKhm1iBAMxAAH7IJosPhgBKhm5iBQIwAwH4IZssPhgCKBm6iRUIwAwE4IdssvhgCKBk6CZWIAAzEIAfssnigyGAkqGbWIEAzEAAfsgmiw+GAEqGbmIFAjADAfghmyw+GAIoGbqJFQjADATgh2yy+GAIoGToJlYgADMQgB+yyeKDIYCSoZtYgQDMyC4A3WoLER8MAZTJzBzZYAigI/49sSK+TxoRHwwBlMnMHNlgCKAj/j2xIr5PGhEfDAGUycwc2WAIoCP+PbEivk8aER8MAZTJzBzZYAigI/49sSK+TxoRHwwBlMnMHNlgCKAj/j2xIr5PGhEfDAGUycwc2WAIoCP+PbEivk8aER8MAZTJzBzZYAigI/49sSK+TxoRHwwBlMnMHNlgCKAj/j2xIr5PGhEfDAGUycwc2WAIoCP+PbEivk8aER8MAZTJzBzZYCEgAD/iq21EfDAEUCYzc2SDhYAA/IivthHxwRBAmczMkQ0WAgLwI77aRsQHQwCFmTNC4oMhAD/iq21EfDAEUJg5IyQ+GALwI77aRsQHQwCFmTNC4oMhAD/iq21EfDAEUJg5IyQ+GALwI77aRsQHQwCFmTNC4oMhAD84lsEMBFAmM3OUiA+GAPxAAGYggDKZmaNEfDAE4AcCMAMBlMnMHCXigyEAPxCAGQigTGbmKBEfDAH4gQDMQABlMjNHifhgCMAPBGAGAiiTmTlKxAdDAH4gADMQQJnMzFEiPhgC8GMiAshwVXMEIDzAhIgPhgD8QABmIICSoQxWxPdJI+KDIQA/EIAZCKBkKIMV8X3SiPhgCMAPBGAGAigZymBFfJ80Ij4YAvADAZiBAEqGMlgR3yeNiA+GAPxAAGYggJKhDFbE90kj4oMhAD8QgBkIoGQogxXxfdKI+GAIwA8EYAYCKBnKYEV8nzQiPhgC8AMBmIEASoYyWBHfJ42ID4YA/EAAZiCAkqEMVsT3SSPigyEAPxCAGQigZCiDFfF90oj4YAjADwRgBgIoGcpgRXyfNCI+GALwAwGYgQBKhjJYEd8njYgPllwAl5dC4xgBmIEASoYyWBHfJ42ID5ZaAJdL4tamJpvV1klVMnQeBFAydBMrZJPFB8ssgMtLLQPsSRa4NhVBACXXzFFDNll8sMQCuLwUM8C+ZJHrUw0EUFLNHDlkk8UHyyuAy0s1A+xNFrpGlUAAJdPM0UM2WXwwBODHIplssAUIIIj4bmKFbLL4YGkFcNkndq1qgADMQAAlQzexQjZZfDAE4Mc8mWywJQggiPhuYoVssvhgCMAPBGAGAigZuokVssnigyEAPxCAGQigZOgmVsgmiw+WVgCym8oRgAEIoGToJlbIJosPhgD8YCewGQigZOgmVsgmiw+GAPyYhgBCMiGAkqGbWCGbLD5YXgHIni8lLYCgWAigZOgmVsgmiw+WWACyV0xQ6/8JSoYASoZuYoVssvhgmQUge800sf6f4EsbAigZuokVssnig6UWgOxVk7X6f4LdNgigZOgmVsgmiw+WXACyt7XQ6f4FAcxXwf8le8R3Eytkk8UHQwB+qN8R7LKP3xoggJKhm1ghmyw+GALwAwGYgQBKhm5ihWyy+GAIwA8EYAYCKBm6iRWyyeKDIQA/EIAZCKBk6CZWyCaLD4YA/FAXADuBY4nvJlbIJosPhgD8QABmIICSoZtYIZssPhgC8AMBmIEASoZuYoVssvhgCMAPeQFwJnAo8d3ECtlk8cEQgB/6AuBaQJHEdxMrZJPFB0MAfkxAAFwNNJD4bmKFbLL4YAjAjykIgPsBxBHfTayQTRYfDAH4MQ0BcEewKOK7iRWyyeKDIQA/EIAZCKBk6CZWyCaLD4YA/EAAZiCAkqGbWCGbLD4YAvADAZiBAEqGbmKFbLL4YAjADwRgBgIoGbqJFbLJ4oMhAD8QgBkIoGToJlbIJosPhgD8QABmIICSoZtYIZssPhgC8AMBmIEASoZuYoVssvhgCMAPBGAGAigZuokVssnigyEAP+KrbUR8yRBAER5fusnigyEAP+KrbUR8yRBAER5fusnigyEAP+KrbUR8yRBAER5fusnigyEAP+KrbUR8yRBAER5fusnigyEAP+KrbUR8yRBAER5fusnigyEAP+KrbUR8yRBAER5fusnigyEAP+KrbUR8yRBAER5fusnigyEAP+KrbUR8yRBAER5fusnigyEAP+KrbUR8yRBAER5fusnigyEAP+KrbUR8yRBAER5fusnigyEAP+KrbUR8yRBAER5fusnigyEAP+KrbUR8yRBAER5fusnigyEAP+KrbUR8yRBAER5fusnigyEAP+KrbUR8yRBAER5fusnigyEAP+KrbUR8yRBAER5fusnig2UXgBKyyeKDIYCSoQxWyCaLD4YA/JBNFh8MAZQMZbBCNll8MATgh2yy+GAIoGQogxWyyeKDIQA/ZJPFB0MAJUMZrJBNFh8MAfghmyw+GAIoGcpghWyy+GAIwA/ZZPHBEEDJUAYrZJPFB0MAfsgmiw+GAEqGMlghmyw+GALwQzZZfDAEUDKUwQrZZPHBEIAfssnigyGAkqEMVsgmiw+GAPyQTRYfDAGUDGWwQjZZfDAE4IdssvhgCKBkKIMVssnigyEAP2STxQdDACVDGayQTRYfDAH4IZssPhgCKBnKYIVssvhgCMAP2WTxwRBAyVAGK2STxQdDAH7IJosPhgBKhjJYIZssPhgC8EM2WXwwBFAylMEK2WTxwRCAH7LJ4oMhgJKhDFbIJosPhgD8kE0WHwwBlAxlsEI2WXwwBOCHbLL4YAigZCiDFbLJ4oMhAD9kk8UHQwAlQxmskE0WHwwB+CGbLD4YAigZymCFbLL4YAjAD9lk8cEQQMlQBitkk8UHQwB+yCaLD4YASoYyWCGbLD4YAvBDNll8MARQMpTBCtlk8cEQgB+yyeKDIYCSoQxWyCaLD4YA/JBNFh8MAZQMZbBCNll8MATgh2yy+GAIoGQogxWyyeKDIQA/ZJPFB0MAJUMZrJBNFh8MAfghmyw+GAIoGcpghWyy+GAIwA/ZZPHBEEDJUAYrZJPFB0MAfsgmiw+GAEqGMlghmyw+GALwQzZZfDAEUDKUwQrZZPHBEIAfssnigyGAkqEMVsgmiw+GAPyQTRYfLIMAAMYIwxgE2B7GCABgGAxjEGB7GAcIIJ74b+9WyCaTDfYAdN8T2WT5giEAKWSTyQZ7ALrviWyyfMEQgBSyyWSDPQDd90Q2Wb5gCEAK2WSywR6A7nsimyxfMAQghWwy2WAPQPc9kU2WLxgCkEI2mWywB6D7nsgmyxdskgIAAAAEAAAwWRAAAMBEQQAAABMFAQAATBQEAAAwURAAAMBEQQAAABNFUgC//fBF0zRP3+7/9banlZ9fNM3RV+/dVvM+vPuiXcdfl79+eLk3WfnwzUnTPHq1yHLRLHjitp5D+fi8efbghYwm7WEwjEdT2PEOY0UBXJ0s3rpn+3697Wk35/Pfjr7zXt/h3LzZXserxa+ve897t4j2eD6YzrVnznjSHgTDeDyFHe8wFhTA9Unz+Y8zn85G2evdX297WvuGP35bbr7PPHXOm6NXs1V+0Xz2U/vrLMJslWcJeqs8m1Cfz6L8eTF4Pj7PG6nCzBlR2kNgGI+osOMdxoICuFiMq9nnjFamvV9ve9psFD5ZPJ7vA8aC2Tp2k3w53M7niWYJtlZ5lXQR8frkcdoNAg+fOWNKewgM4xEVdrzDWE8A63F01X6k6P1629Nm7/vir4k/aCwn+2zKtOs+G3XzD4NXy8fL4tdFgsVMu8rbCyrMnDGlPQCG8ZgKO95hrCeANdcnW3Og92vv8fPlB6vZlOpvi0zHfOasJsxqCi3/uv3h4SJXoN9+OG03+H7Z7fNrZ86Hbzb3Wv7c7RD8tv1xsybddPhl/bcVydM+HIbxnGSFFRnGygLY/qzU/7X3+PnKuOcVdujbsviQcLExsDZWefu3Geef/Ut7fMHTHx1X8XZWOy27DLOZ87uTjT2CN98v/tjOpP6nouXfNqdK8rQVYBjPyVVYlWGsLIDz7a+UvV97j69mTn9TZD5+PpmPnZ1PFnPaD1I37XF2j14tf99//EgIs7V5PPvQdPPupHvb25Vr9wi+W2Vq50w7f55sfLGebzS4aJrfz/72t5PxpK0Bw3j5e6LCygxjYQFcb73D/V/7j19sfBHNPHO6Q+h+382YzU97G6s8C/TqzcZnjNnvR1+/L799n2LqrN7n+dbqdqB333Vna/ls+d/VX7d2D7a5ur9tfQbOnbYCDOOMhZUZxroCmL3dmx+Ver/uPL5Rs9Qz5+N/Op19M/xy/jHjtpmz+fmjPb7s9eJZ+z87xnC1nDmr455nP2z2r2erOTL/6rzaVLAVdyRp7w3DOHdhxz6MZQVws33A9E3/+Omdx9ff2lLPnJbFl+dPzJzNzx8rqpyuWIWbX/7fPzTLmbNYp3bSbGwE6CbS4q/dfNrYSnqxsfl0BGkfAsM4cWEVhrGqANoZ8ezWX/c9vjw98dl5+pmz+Hxw+8xZDKze4EmyWbg7PmK5v2x95Ef7MWlndnTzZ77e662iM9afirKnfRgM45K1sCLDWFQAt06c68199dtPu/mh3ev+dmd/fELmo2J779kq2XorcX/wpOgJ3SnuR7/79t2embMx+OffortDBOdfnW+fOZnTPgyG8fppa1IUVmUYawrg44tmsael/+vWzOk9bfHkNN8wP0E3KraPn1sl2whwnm/mXDTNl92hbVdDPjp1f75YHmixry650z4MhvHG03r/KhiZYSwpgA/Pm80LS/V+vePx2060ycT8U8ItZ9CsP0L0Pkxk+FC4XqWLARtPu+G/ePCW78Kp0z4MhnH/p+Wv4YXVGcaKAphVY3Pw93695fHVQVkXqQ4y2OJ8ezvhbefQ945BWP2r5TVYIlnNlJs3y5mzGO6LT4Nbq95F+/9Olpc521uYzGkfBMM4b2F1hrGgAG62Lyt4s3OVwb2PL4+fWx1Hl5CNY8O6Vb/lKlqr7Ynniw2Qi7MTe5cRC2G1rlervWfztZtn6x1A3f39D+uLRj7ZXkbZejhh2ofAME5cWJ1hLCiAi+0j5S72Hzi38/j8GrU/nyQYXrfRTvZX78tv3zSrWb73OrrtBspfu0MBny3/VXf13fhPTt3Os98vDlNc7hO79RTKxfM3fmyvvPLLi+28mdM+AIZx5sLKDGM9AWztZn/W//W2p813pbV8nvWbc1mv4zLIu8WBaP0xsroSSffbh8W/OtrZUxjA8n3//P86WXz//9PJxvu+dRGVlqtmNQVWf+vlTZz2/jCMUxdWZhjrCeB6dZWmboT1fr3taaU9fq69FMfXaT84tXSXC1lcgbDltnvpbV9vsEu28a9Cufn+pHubV4dFv+4uo7h63+eXUVxdA2v2hPU20+5vj9a3ElyQOO29YRiX1IVVGcZ6AgAAgEEgAACAiYIAAAAmCgIAAJgoCAAAYKIgAACAiYIAAAAmCgIAAJgoCAAAYKIgAACAiYIAAAAmCgIAAJgoCAAAYKIgAACAiYIAAAAmCgIAAJgoCAAAYKIgAACAiYIAAAAmyv8P2Q6Ff6qbfSYAAAAASUVORK5CYII=" class="img-fluid figure-img" width="768">
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-fig_d9-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure 17: Predicted probabilities of voting for a radical right party by education, age group and gender (Appendix)
</figcaption>
</figure>
</div>
</div>
</div>
</section>
<section id="marginal-effects-of-women-vs.-men-on-voting-for-the-radical-right-across-cohorts-and-education-levels" class="level2" data-number="4.8">
<h2 data-number="4.8" class="anchored" data-anchor-id="marginal-effects-of-women-vs.-men-on-voting-for-the-radical-right-across-cohorts-and-education-levels"><span class="header-section-number">4.8</span> Marginal effects of women vs. men on voting for the radical right across cohorts and education levels</h2>
<div class="cell">
<details class="code-fold">
<summary>Show the code</summary>
<div class="sourceCode cell-code" id="cb23"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a>me_three_rr_gender <span class="ot">&lt;-</span> <span class="fu">avg_slopes</span>(</span>
<span id="cb23-2"><a href="#cb23-2" aria-hidden="true" tabindex="-1"></a>  three_rr,</span>
<span id="cb23-3"><a href="#cb23-3" aria-hidden="true" tabindex="-1"></a>  <span class="at">variables =</span> <span class="st">&quot;gndr_fct&quot;</span>,</span>
<span id="cb23-4"><a href="#cb23-4" aria-hidden="true" tabindex="-1"></a>  <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&quot;cohort_fct&quot;</span>, <span class="st">&quot;edu_fct&quot;</span>),</span>
<span id="cb23-5"><a href="#cb23-5" aria-hidden="true" tabindex="-1"></a>  <span class="at">newdata =</span> <span class="fu">datagrid</span>(</span>
<span id="cb23-6"><a href="#cb23-6" aria-hidden="true" tabindex="-1"></a>    <span class="at">gndr_fct =</span> <span class="fu">c</span>(<span class="st">&quot;men&quot;</span>, <span class="st">&quot;women&quot;</span>),</span>
<span id="cb23-7"><a href="#cb23-7" aria-hidden="true" tabindex="-1"></a>    <span class="at">cohort_fct =</span> <span class="fu">c</span>(</span>
<span id="cb23-8"><a href="#cb23-8" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;interwar&quot;</span>,</span>
<span id="cb23-9"><a href="#cb23-9" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;post-war&quot;</span>,</span>
<span id="cb23-10"><a href="#cb23-10" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;60-70s&quot;</span>,</span>
<span id="cb23-11"><a href="#cb23-11" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;80s&quot;</span>,</span>
<span id="cb23-12"><a href="#cb23-12" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;90s&quot;</span>,</span>
<span id="cb23-13"><a href="#cb23-13" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;millennial&quot;</span></span>
<span id="cb23-14"><a href="#cb23-14" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb23-15"><a href="#cb23-15" aria-hidden="true" tabindex="-1"></a>    <span class="at">edu_fct =</span> <span class="fu">c</span>(</span>
<span id="cb23-16"><a href="#cb23-16" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;(below) lower secondary&quot;</span>,</span>
<span id="cb23-17"><a href="#cb23-17" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;upper secondary&quot;</span>,</span>
<span id="cb23-18"><a href="#cb23-18" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;tertiary&quot;</span></span>
<span id="cb23-19"><a href="#cb23-19" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb23-20"><a href="#cb23-20" aria-hidden="true" tabindex="-1"></a>    <span class="at">grid_type =</span> <span class="st">&quot;counterfactual&quot;</span></span>
<span id="cb23-21"><a href="#cb23-21" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb23-22"><a href="#cb23-22" aria-hidden="true" tabindex="-1"></a>    <span class="at">re.form =</span> <span class="cn">NA</span></span>
<span id="cb23-23"><a href="#cb23-23" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb23-24"><a href="#cb23-24" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb23-25"><a href="#cb23-25" aria-hidden="true" tabindex="-1"></a>me_three_rr_gender <span class="ot">&lt;-</span> me_three_rr_gender <span class="sc">%&gt;%</span></span>
<span id="cb23-26"><a href="#cb23-26" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">contrast =</span> <span class="fu">case_when</span>(</span>
<span id="cb23-27"><a href="#cb23-27" aria-hidden="true" tabindex="-1"></a>    contrast <span class="sc">==</span> <span class="st">&quot;women - men&quot;</span> <span class="sc">~</span></span>
<span id="cb23-28"><a href="#cb23-28" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;women vs men&quot;</span></span>
<span id="cb23-29"><a href="#cb23-29" aria-hidden="true" tabindex="-1"></a>  ))</span>
<span id="cb23-30"><a href="#cb23-30" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb23-31"><a href="#cb23-31" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> me_three_rr_gender, <span class="fu">aes</span>(</span>
<span id="cb23-32"><a href="#cb23-32" aria-hidden="true" tabindex="-1"></a>  <span class="at">x =</span> cohort_fct, <span class="at">y =</span> estimate,</span>
<span id="cb23-33"><a href="#cb23-33" aria-hidden="true" tabindex="-1"></a>  <span class="at">ymin =</span> conf.low, <span class="at">ymax =</span> conf.high,</span>
<span id="cb23-34"><a href="#cb23-34" aria-hidden="true" tabindex="-1"></a>  <span class="at">color =</span> edu_fct</span>
<span id="cb23-35"><a href="#cb23-35" aria-hidden="true" tabindex="-1"></a>)) <span class="sc">+</span></span>
<span id="cb23-36"><a href="#cb23-36" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept =</span> <span class="dv">0</span>, <span class="at">color =</span> <span class="st">&quot;grey70&quot;</span>) <span class="sc">+</span></span>
<span id="cb23-37"><a href="#cb23-37" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_pointrange</span>(<span class="at">position =</span> <span class="fu">position_dodge</span>(<span class="at">width =</span> <span class="fu">c</span>(<span class="fl">0.2</span>))) <span class="sc">+</span></span>
<span id="cb23-38"><a href="#cb23-38" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb23-39"><a href="#cb23-39" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> <span class="cn">NULL</span>, <span class="at">color =</span> <span class="st">&quot;Education&quot;</span>,</span>
<span id="cb23-40"><a href="#cb23-40" aria-hidden="true" tabindex="-1"></a>    <span class="at">y =</span> <span class="st">&quot;Marginal effect of women vs. men&quot;</span></span>
<span id="cb23-41"><a href="#cb23-41" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb23-42"><a href="#cb23-42" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_viridis_d</span>(<span class="at">option =</span> <span class="st">&quot;plasma&quot;</span>, <span class="at">end =</span> .<span class="dv">8</span>) <span class="sc">+</span></span>
<span id="cb23-43"><a href="#cb23-43" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_light</span>() <span class="sc">+</span></span>
<span id="cb23-44"><a href="#cb23-44" aria-hidden="true" tabindex="-1"></a>  <span class="fu">my_theme</span>() <span class="sc">+</span></span>
<span id="cb23-45"><a href="#cb23-45" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;top&quot;</span>)</span>
<span id="cb23-46"><a href="#cb23-46" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb23-47"><a href="#cb23-47" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(</span>
<span id="cb23-48"><a href="#cb23-48" aria-hidden="true" tabindex="-1"></a>  <span class="at">filename =</span> <span class="fu">here</span>(<span class="st">&quot;figs&quot;</span>, <span class="st">&quot;final&quot;</span>, <span class="st">&quot;appendix_fig_d10.png&quot;</span>),</span>
<span id="cb23-49"><a href="#cb23-49" aria-hidden="true" tabindex="-1"></a>  <span class="at">units =</span> <span class="fu">c</span>(<span class="st">&quot;in&quot;</span>),</span>
<span id="cb23-50"><a href="#cb23-50" aria-hidden="true" tabindex="-1"></a>  <span class="at">width =</span> <span class="dv">6</span>, <span class="at">height =</span> <span class="dv">5</span>,</span>
<span id="cb23-51"><a href="#cb23-51" aria-hidden="true" tabindex="-1"></a>  <span class="at">dpi =</span> <span class="dv">300</span></span>
<span id="cb23-52"><a href="#cb23-52" aria-hidden="true" tabindex="-1"></a>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<div id="fig-fig_d10" class="quarto-float quarto-figure quarto-figure-center anchored">
<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-fig_d10-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<img role="img" 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" class="img-fluid figure-img" width="768">
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-fig_d10-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure 18: Marginal effects of women vs. men on voting for the radical right across cohorts and education levels (Appendix)
</figcaption>
</figure>
</div>
</div>
</div>
</section>
</section>
<section id="tables" class="level1" data-number="5">
<h1 data-number="5"><span class="header-section-number">5</span> Tables</h1>
<section id="preliminaries" class="level2" data-number="5.1">
<h2 data-number="5.1" class="anchored" data-anchor-id="preliminaries"><span class="header-section-number">5.1</span> Preliminaries</h2>
<div class="cell">
<details class="code-fold">
<summary>Show the code</summary>
<div class="sourceCode cell-code" id="cb24"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a>coefname <span class="ot">&lt;-</span> <span class="fu">c</span>(</span>
<span id="cb24-2"><a href="#cb24-2" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;cohort_fctpost-war&quot;</span> <span class="ot">=</span> <span class="st">&quot;Post-War Generation&quot;</span>,</span>
<span id="cb24-3"><a href="#cb24-3" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;cohort_fct60-70s&quot;</span> <span class="ot">=</span> <span class="st">&quot;60-70s Generation&quot;</span>,</span>
<span id="cb24-4"><a href="#cb24-4" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;cohort_fct80s&quot;</span> <span class="ot">=</span> <span class="st">&quot;80s Generation&quot;</span>,</span>
<span id="cb24-5"><a href="#cb24-5" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;cohort_fct90s&quot;</span> <span class="ot">=</span> <span class="st">&quot;90s Generation&quot;</span>,</span>
<span id="cb24-6"><a href="#cb24-6" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;cohort_fctmillennial&quot;</span> <span class="ot">=</span> <span class="st">&quot;Millennials&quot;</span>,</span>
<span id="cb24-7"><a href="#cb24-7" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;gndr_fctwomen&quot;</span> <span class="ot">=</span> <span class="st">&quot;Gender: female&quot;</span>,</span>
<span id="cb24-8"><a href="#cb24-8" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;factor(age_group)2&quot;</span> <span class="ot">=</span> <span class="st">&quot;Age: 30-65&quot;</span>,</span>
<span id="cb24-9"><a href="#cb24-9" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;factor(age_group)3&quot;</span> <span class="ot">=</span> <span class="st">&quot;Age: 66+&quot;</span>,</span>
<span id="cb24-10"><a href="#cb24-10" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;immigrant&quot;</span> <span class="ot">=</span> <span class="st">&quot;Immigrant background&quot;</span>,</span>
<span id="cb24-11"><a href="#cb24-11" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;edu_fctupper secondary&quot;</span> <span class="ot">=</span> <span class="st">&quot;Education: upper secondary&quot;</span>,</span>
<span id="cb24-12"><a href="#cb24-12" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;edu_fcttertiary&quot;</span> <span class="ot">=</span> <span class="st">&quot;Education: tertiary&quot;</span>,</span>
<span id="cb24-13"><a href="#cb24-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb24-14"><a href="#cb24-14" aria-hidden="true" tabindex="-1"></a>  <span class="co"># cohort # education</span></span>
<span id="cb24-15"><a href="#cb24-15" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;cohort_fctpost-war:edu_fctupper secondary&quot;</span> <span class="ot">=</span></span>
<span id="cb24-16"><a href="#cb24-16" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;Post-War Generation # education: upper secondary&quot;</span>,</span>
<span id="cb24-17"><a href="#cb24-17" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;cohort_fctpost-war:edu_fcttertiary&quot;</span> <span class="ot">=</span></span>
<span id="cb24-18"><a href="#cb24-18" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;Post-War Generation # education: tertiary&quot;</span>,</span>
<span id="cb24-19"><a href="#cb24-19" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;cohort_fct60-70s:edu_fctupper secondary&quot;</span> <span class="ot">=</span></span>
<span id="cb24-20"><a href="#cb24-20" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;60-70s Generation # education: upper secondary&quot;</span>,</span>
<span id="cb24-21"><a href="#cb24-21" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;cohort_fct60-70s:edu_fcttertiary&quot;</span> <span class="ot">=</span></span>
<span id="cb24-22"><a href="#cb24-22" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;60-70s Generation # education: tertiary&quot;</span>,</span>
<span id="cb24-23"><a href="#cb24-23" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;cohort_fct80s:edu_fctupper secondary&quot;</span> <span class="ot">=</span></span>
<span id="cb24-24"><a href="#cb24-24" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;80s Generation # education: upper secondary&quot;</span>,</span>
<span id="cb24-25"><a href="#cb24-25" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;cohort_fct80s:edu_fcttertiary&quot;</span> <span class="ot">=</span></span>
<span id="cb24-26"><a href="#cb24-26" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;80s Generation # education: tertiary&quot;</span>,</span>
<span id="cb24-27"><a href="#cb24-27" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;cohort_fctmillennial:edu_fctupper secondary&quot;</span> <span class="ot">=</span></span>
<span id="cb24-28"><a href="#cb24-28" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;Millennials # education: upper secondary&quot;</span>,</span>
<span id="cb24-29"><a href="#cb24-29" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;cohort_fctmillennial:edu_fcttertiary&quot;</span> <span class="ot">=</span></span>
<span id="cb24-30"><a href="#cb24-30" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;Millennials # education: tertiary&quot;</span>,</span>
<span id="cb24-31"><a href="#cb24-31" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb24-32"><a href="#cb24-32" aria-hidden="true" tabindex="-1"></a>  <span class="co"># cohort # gender</span></span>
<span id="cb24-33"><a href="#cb24-33" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;cohort_fctpost-war:gndr_fctwomen&quot;</span> <span class="ot">=</span></span>
<span id="cb24-34"><a href="#cb24-34" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;Post-War Generation # Female&quot;</span>,</span>
<span id="cb24-35"><a href="#cb24-35" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;cohort_fct60-70s:gndr_fctwomen&quot;</span> <span class="ot">=</span></span>
<span id="cb24-36"><a href="#cb24-36" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;60-70s Generation # Female&quot;</span>,</span>
<span id="cb24-37"><a href="#cb24-37" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;cohort_fct80s:gndr_fctwomen&quot;</span> <span class="ot">=</span></span>
<span id="cb24-38"><a href="#cb24-38" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;80s Generation # Female&quot;</span>,</span>
<span id="cb24-39"><a href="#cb24-39" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;cohort_fct90s:gndr_fctwomen&quot;</span> <span class="ot">=</span></span>
<span id="cb24-40"><a href="#cb24-40" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;90s Generation # Female&quot;</span>,</span>
<span id="cb24-41"><a href="#cb24-41" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;cohort_fctmillennial:gndr_fctwomen&quot;</span> <span class="ot">=</span></span>
<span id="cb24-42"><a href="#cb24-42" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;Millennials # Female&quot;</span>,</span>
<span id="cb24-43"><a href="#cb24-43" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb24-44"><a href="#cb24-44" aria-hidden="true" tabindex="-1"></a>  <span class="co"># education # gender</span></span>
<span id="cb24-45"><a href="#cb24-45" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;edu_fctupper secondary:gndr_fctwomen&quot;</span> <span class="ot">=</span></span>
<span id="cb24-46"><a href="#cb24-46" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;Education: upper secondary # Female&quot;</span>,</span>
<span id="cb24-47"><a href="#cb24-47" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;edu_fcttertiary:gndr_fctwomen&quot;</span> <span class="ot">=</span></span>
<span id="cb24-48"><a href="#cb24-48" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;Education: tertiary # female&quot;</span>,</span>
<span id="cb24-49"><a href="#cb24-49" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb24-50"><a href="#cb24-50" aria-hidden="true" tabindex="-1"></a>  <span class="co"># cohort # education # gender</span></span>
<span id="cb24-51"><a href="#cb24-51" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;cohort_fctpost-war:edu_fctupper secondary:gndr_fctwomen&quot;</span> <span class="ot">=</span></span>
<span id="cb24-52"><a href="#cb24-52" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;Post-War Generation # Educ.: upper secondary # Female&quot;</span>,</span>
<span id="cb24-53"><a href="#cb24-53" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;cohort_fct60-70s:edu_fctupper secondary:gndr_fctwomen&quot;</span> <span class="ot">=</span></span>
<span id="cb24-54"><a href="#cb24-54" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;60-70s Generation # Educ.: upper secondary # Female&quot;</span>,</span>
<span id="cb24-55"><a href="#cb24-55" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;cohort_fct80s:edu_fctupper secondary:gndr_fctwomen&quot;</span> <span class="ot">=</span></span>
<span id="cb24-56"><a href="#cb24-56" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;80s Generation # Educ.: upper secondary # Female&quot;</span>,</span>
<span id="cb24-57"><a href="#cb24-57" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;cohort_fct90s:edu_fctupper secondary:gndr_fctwomen&quot;</span> <span class="ot">=</span></span>
<span id="cb24-58"><a href="#cb24-58" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;90s Generation # Educ.: upper secondary # Female&quot;</span>,</span>
<span id="cb24-59"><a href="#cb24-59" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;cohort_fctmillennial:edu_fctupper secondary:gndr_fctwomen&quot;</span> <span class="ot">=</span></span>
<span id="cb24-60"><a href="#cb24-60" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;Millennials # Educ.: upper secondary # Female&quot;</span>,</span>
<span id="cb24-61"><a href="#cb24-61" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;cohort_fctpost-war:edu_fcttertiary:gndr_fctwomen&quot;</span> <span class="ot">=</span></span>
<span id="cb24-62"><a href="#cb24-62" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;Post-War Generation # Educ.: tertiary # Female&quot;</span>,</span>
<span id="cb24-63"><a href="#cb24-63" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;cohort_fct60-70s:edu_fcttertiary:gndr_fctwomen&quot;</span> <span class="ot">=</span></span>
<span id="cb24-64"><a href="#cb24-64" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;60-70s Generation # Educ.: tertiary # Female&quot;</span>,</span>
<span id="cb24-65"><a href="#cb24-65" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;cohort_fct80s:edu_fcttertiary:gndr_fctwomen&quot;</span> <span class="ot">=</span></span>
<span id="cb24-66"><a href="#cb24-66" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;80s Generation # Educ.: tertiary # Female&quot;</span>,</span>
<span id="cb24-67"><a href="#cb24-67" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;cohort_fct90s:edu_fcttertiary:gndr_fctwomen&quot;</span> <span class="ot">=</span></span>
<span id="cb24-68"><a href="#cb24-68" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;90s Generation # Educ.: tertiary # Female&quot;</span>,</span>
<span id="cb24-69"><a href="#cb24-69" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;cohort_fctmillennial:edu_fcttertiary:gndr_fctwomen&quot;</span> <span class="ot">=</span></span>
<span id="cb24-70"><a href="#cb24-70" aria-hidden="true" tabindex="-1"></a>    <span class="st">&quot;Millennials # Educ.: tertiary # Female&quot;</span></span>
<span id="cb24-71"><a href="#cb24-71" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb24-72"><a href="#cb24-72" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb24-73"><a href="#cb24-73" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb24-74"><a href="#cb24-74" aria-hidden="true" tabindex="-1"></a><span class="co"># add rows</span></span>
<span id="cb24-75"><a href="#cb24-75" aria-hidden="true" tabindex="-1"></a>rows <span class="ot">&lt;-</span> <span class="fu">tribble</span>(</span>
<span id="cb24-76"><a href="#cb24-76" aria-hidden="true" tabindex="-1"></a>  <span class="sc">~</span>term, <span class="sc">~</span><span class="st">&quot;Model 1&quot;</span>, <span class="sc">~</span><span class="st">&quot;Model 2&quot;</span>, <span class="sc">~</span><span class="st">&quot;Model 3&quot;</span>, <span class="sc">~</span><span class="st">&quot;Model 4&quot;</span>,</span>
<span id="cb24-77"><a href="#cb24-77" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Interwar Generation&quot;</span>, <span class="st">&quot;ref.&quot;</span>, <span class="st">&quot;ref.&quot;</span>, <span class="st">&quot;ref.&quot;</span>, <span class="st">&quot;ref.&quot;</span>,</span>
<span id="cb24-78"><a href="#cb24-78" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Gender: male&quot;</span>, <span class="st">&quot;ref.&quot;</span>, <span class="st">&quot;ref.&quot;</span>, <span class="st">&quot;ref.&quot;</span>, <span class="st">&quot;ref.&quot;</span>,</span>
<span id="cb24-79"><a href="#cb24-79" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Age: 22-29&quot;</span>, <span class="st">&quot;ref.&quot;</span>, <span class="st">&quot;ref.&quot;</span>, <span class="st">&quot;ref.&quot;</span>, <span class="st">&quot;ref.&quot;</span>,</span>
<span id="cb24-80"><a href="#cb24-80" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Education: (Below) lower secondary&quot;</span>, <span class="st">&quot; &quot;</span>, <span class="st">&quot;ref.&quot;</span>, <span class="st">&quot;ref.&quot;</span>, <span class="st">&quot;ref.&quot;</span>,</span>
<span id="cb24-81"><a href="#cb24-81" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Generation X Education&quot;</span>, <span class="st">&quot;&quot;</span>, <span class="st">&quot;&quot;</span>, <span class="st">&quot;&quot;</span>, <span class="st">&quot;&quot;</span>,</span>
<span id="cb24-82"><a href="#cb24-82" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Generation X Female&quot;</span>, <span class="st">&quot;&quot;</span>, <span class="st">&quot;&quot;</span>, <span class="st">&quot;&quot;</span>, <span class="st">&quot;&quot;</span>,</span>
<span id="cb24-83"><a href="#cb24-83" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Education X Female&quot;</span>, <span class="st">&quot;&quot;</span>, <span class="st">&quot;&quot;</span>, <span class="st">&quot;&quot;</span>, <span class="st">&quot;&quot;</span>,</span>
<span id="cb24-84"><a href="#cb24-84" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Generation X Education X Female&quot;</span>, <span class="st">&quot;&quot;</span>, <span class="st">&quot;&quot;</span>, <span class="st">&quot;&quot;</span>, <span class="st">&quot;&quot;</span>,</span>
<span id="cb24-85"><a href="#cb24-85" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Country fixed effects&quot;</span>, <span class="st">&quot;yes&quot;</span>, <span class="st">&quot;yes&quot;</span>, <span class="st">&quot;yes&quot;</span>, <span class="st">&quot;yes&quot;</span>,</span>
<span id="cb24-86"><a href="#cb24-86" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Twoway interaction Age group X Education&quot;</span>, <span class="st">&quot;no&quot;</span>, <span class="st">&quot;no&quot;</span>, <span class="st">&quot;yes&quot;</span>, <span class="st">&quot;yes&quot;</span>,</span>
<span id="cb24-87"><a href="#cb24-87" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Twoway interaction ESS wave X Education&quot;</span>, <span class="st">&quot;no&quot;</span>, <span class="st">&quot;no&quot;</span>, <span class="st">&quot;yes&quot;</span>, <span class="st">&quot;yes&quot;</span>,</span>
<span id="cb24-88"><a href="#cb24-88" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Threeway interaction Age group X Education X Gender&quot;</span>, <span class="st">&quot;no&quot;</span>, <span class="st">&quot;no&quot;</span>, <span class="st">&quot;no&quot;</span>, <span class="st">&quot;yes&quot;</span>,</span>
<span id="cb24-89"><a href="#cb24-89" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Threeway interaction ESS wave X Education X Gender&quot;</span>, <span class="st">&quot;no&quot;</span>, <span class="st">&quot;no&quot;</span>, <span class="st">&quot;no&quot;</span>, <span class="st">&quot;yes&quot;</span></span>
<span id="cb24-90"><a href="#cb24-90" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb24-91"><a href="#cb24-91" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb24-92"><a href="#cb24-92" aria-hidden="true" tabindex="-1"></a><span class="co"># position of additional rows</span></span>
<span id="cb24-93"><a href="#cb24-93" aria-hidden="true" tabindex="-1"></a><span class="fu">attr</span>(rows, <span class="st">&quot;position&quot;</span>) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">12</span>, <span class="dv">15</span>, <span class="dv">22</span>, <span class="dv">27</span>, <span class="dv">44</span>, <span class="dv">55</span>, <span class="dv">60</span>, <span class="dv">81</span>, <span class="dv">82</span>, <span class="dv">83</span>, <span class="dv">84</span>, <span class="dv">85</span>)</span>
<span id="cb24-94"><a href="#cb24-94" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb24-95"><a href="#cb24-95" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb24-96"><a href="#cb24-96" aria-hidden="true" tabindex="-1"></a>gof <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;nobs&quot;</span>, <span class="st">&quot;r.squared&quot;</span>, <span class="st">&quot;AIC&quot;</span>, <span class="st">&quot;BIC&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
</div>
</section>
<section id="all-models-of-green-vote" class="level2" data-number="5.2">
<h2 data-number="5.2" class="anchored" data-anchor-id="all-models-of-green-vote"><span class="header-section-number">5.2</span> All models of green vote</h2>
<div id="tbl-table_b1" class="cell anchored">
<details class="code-fold">
<summary>Show the code</summary>
<div class="sourceCode cell-code" id="cb25"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a>models <span class="ot">&lt;-</span> <span class="fu">list</span>(</span>
<span id="cb25-2"><a href="#cb25-2" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Model 1&quot;</span> <span class="ot">=</span> <span class="fu">glmer</span>(</span>
<span id="cb25-3"><a href="#cb25-3" aria-hidden="true" tabindex="-1"></a>    vote_green <span class="sc">~</span> cohort_fct <span class="sc">+</span> gndr_fct <span class="sc">+</span></span>
<span id="cb25-4"><a href="#cb25-4" aria-hidden="true" tabindex="-1"></a>      <span class="sc">+</span><span class="fu">factor</span>(age_group) <span class="sc">+</span></span>
<span id="cb25-5"><a href="#cb25-5" aria-hidden="true" tabindex="-1"></a>      <span class="fu">factor</span>(essround) <span class="sc">+</span> <span class="fu">factor</span>(cntry_id) <span class="sc">+</span></span>
<span id="cb25-6"><a href="#cb25-6" aria-hidden="true" tabindex="-1"></a>      (<span class="dv">1</span> <span class="sc">|</span> cntry_wave_id),</span>
<span id="cb25-7"><a href="#cb25-7" aria-hidden="true" tabindex="-1"></a>    <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">&quot;logit&quot;</span>),</span>
<span id="cb25-8"><a href="#cb25-8" aria-hidden="true" tabindex="-1"></a>    <span class="at">nAGQ =</span> <span class="dv">0</span>,</span>
<span id="cb25-9"><a href="#cb25-9" aria-hidden="true" tabindex="-1"></a>    <span class="at">data =</span> dfgreen</span>
<span id="cb25-10"><a href="#cb25-10" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb25-11"><a href="#cb25-11" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Model 2&quot;</span> <span class="ot">=</span> <span class="fu">glmer</span>(</span>
<span id="cb25-12"><a href="#cb25-12" aria-hidden="true" tabindex="-1"></a>    vote_green <span class="sc">~</span> cohort_fct <span class="sc">+</span> gndr_fct <span class="sc">+</span> edu_fct <span class="sc">+</span></span>
<span id="cb25-13"><a href="#cb25-13" aria-hidden="true" tabindex="-1"></a>      <span class="fu">factor</span>(age_group) <span class="sc">+</span> immigrant <span class="sc">+</span></span>
<span id="cb25-14"><a href="#cb25-14" aria-hidden="true" tabindex="-1"></a>      <span class="fu">factor</span>(essround) <span class="sc">+</span> <span class="fu">factor</span>(cntry_id) <span class="sc">+</span></span>
<span id="cb25-15"><a href="#cb25-15" aria-hidden="true" tabindex="-1"></a>      (<span class="dv">1</span> <span class="sc">|</span> cntry_wave_id),</span>
<span id="cb25-16"><a href="#cb25-16" aria-hidden="true" tabindex="-1"></a>    <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">&quot;logit&quot;</span>),</span>
<span id="cb25-17"><a href="#cb25-17" aria-hidden="true" tabindex="-1"></a>    <span class="at">nAGQ =</span> <span class="dv">0</span>,</span>
<span id="cb25-18"><a href="#cb25-18" aria-hidden="true" tabindex="-1"></a>    <span class="at">data =</span> dfgreen</span>
<span id="cb25-19"><a href="#cb25-19" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb25-20"><a href="#cb25-20" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Model 3&quot;</span> <span class="ot">=</span> <span class="fu">glmer</span>(</span>
<span id="cb25-21"><a href="#cb25-21" aria-hidden="true" tabindex="-1"></a>    vote_green <span class="sc">~</span> cohort_fct <span class="sc">*</span> edu_fct <span class="sc">+</span></span>
<span id="cb25-22"><a href="#cb25-22" aria-hidden="true" tabindex="-1"></a>      <span class="fu">factor</span>(age_group) <span class="sc">*</span> edu_fct <span class="sc">+</span></span>
<span id="cb25-23"><a href="#cb25-23" aria-hidden="true" tabindex="-1"></a>      <span class="fu">factor</span>(essround) <span class="sc">*</span> edu_fct <span class="sc">+</span></span>
<span id="cb25-24"><a href="#cb25-24" aria-hidden="true" tabindex="-1"></a>      gndr_fct <span class="sc">+</span> immigrant <span class="sc">+</span></span>
<span id="cb25-25"><a href="#cb25-25" aria-hidden="true" tabindex="-1"></a>      <span class="sc">+</span><span class="fu">factor</span>(cntry_id) <span class="sc">+</span></span>
<span id="cb25-26"><a href="#cb25-26" aria-hidden="true" tabindex="-1"></a>      (<span class="dv">1</span> <span class="sc">|</span> cntry_wave_id),</span>
<span id="cb25-27"><a href="#cb25-27" aria-hidden="true" tabindex="-1"></a>    <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">&quot;logit&quot;</span>),</span>
<span id="cb25-28"><a href="#cb25-28" aria-hidden="true" tabindex="-1"></a>    <span class="at">nAGQ =</span> <span class="dv">0</span>,</span>
<span id="cb25-29"><a href="#cb25-29" aria-hidden="true" tabindex="-1"></a>    <span class="at">data =</span> dfgreen</span>
<span id="cb25-30"><a href="#cb25-30" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb25-31"><a href="#cb25-31" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Model 4&quot;</span> <span class="ot">=</span> <span class="fu">glmer</span>(</span>
<span id="cb25-32"><a href="#cb25-32" aria-hidden="true" tabindex="-1"></a>    vote_green <span class="sc">~</span> cohort_fct <span class="sc">*</span> edu_fct <span class="sc">*</span> gndr_fct <span class="sc">+</span></span>
<span id="cb25-33"><a href="#cb25-33" aria-hidden="true" tabindex="-1"></a>      <span class="fu">factor</span>(age_group) <span class="sc">*</span> edu_fct <span class="sc">*</span> gndr_fct <span class="sc">+</span></span>
<span id="cb25-34"><a href="#cb25-34" aria-hidden="true" tabindex="-1"></a>      <span class="fu">factor</span>(essround) <span class="sc">*</span> edu_fct <span class="sc">*</span> gndr_fct <span class="sc">+</span></span>
<span id="cb25-35"><a href="#cb25-35" aria-hidden="true" tabindex="-1"></a>      immigrant <span class="sc">+</span></span>
<span id="cb25-36"><a href="#cb25-36" aria-hidden="true" tabindex="-1"></a>      <span class="fu">factor</span>(cntry_id) <span class="sc">+</span> (<span class="dv">1</span> <span class="sc">|</span> cntry_wave_id),</span>
<span id="cb25-37"><a href="#cb25-37" aria-hidden="true" tabindex="-1"></a>    <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">&quot;logit&quot;</span>),</span>
<span id="cb25-38"><a href="#cb25-38" aria-hidden="true" tabindex="-1"></a>    <span class="at">nAGQ =</span> <span class="dv">0</span>,</span>
<span id="cb25-39"><a href="#cb25-39" aria-hidden="true" tabindex="-1"></a>    <span class="at">data =</span> dfgreen</span>
<span id="cb25-40"><a href="#cb25-40" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb25-41"><a href="#cb25-41" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb25-42"><a href="#cb25-42" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb25-43"><a href="#cb25-43" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb25-44"><a href="#cb25-44" aria-hidden="true" tabindex="-1"></a><span class="co"># create the table</span></span>
<span id="cb25-45"><a href="#cb25-45" aria-hidden="true" tabindex="-1"></a><span class="fu">modelsummary</span>(models,</span>
<span id="cb25-46"><a href="#cb25-46" aria-hidden="true" tabindex="-1"></a>  <span class="at">stars =</span> <span class="cn">TRUE</span>, <span class="at">fmt =</span> <span class="dv">2</span>,</span>
<span id="cb25-47"><a href="#cb25-47" aria-hidden="true" tabindex="-1"></a>  <span class="at">coef_map =</span> coefname,</span>
<span id="cb25-48"><a href="#cb25-48" aria-hidden="true" tabindex="-1"></a>  <span class="at">add_rows =</span> rows,</span>
<span id="cb25-49"><a href="#cb25-49" aria-hidden="true" tabindex="-1"></a>  <span class="at">gof_omit =</span> <span class="st">&quot;ICC|RMSE&quot;</span>,</span>
<span id="cb25-50"><a href="#cb25-50" aria-hidden="true" tabindex="-1"></a>  <span class="at">output =</span> <span class="fu">here</span>(<span class="st">&quot;tables&quot;</span>, <span class="st">&quot;appendix_table_c1.docx&quot;</span>)</span>
<span id="cb25-51"><a href="#cb25-51" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb25-52"><a href="#cb25-52" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb25-53"><a href="#cb25-53" aria-hidden="true" tabindex="-1"></a><span class="fu">modelsummary</span>(models,</span>
<span id="cb25-54"><a href="#cb25-54" aria-hidden="true" tabindex="-1"></a>  <span class="at">stars =</span> <span class="cn">TRUE</span>, <span class="at">fmt =</span> <span class="dv">2</span>,</span>
<span id="cb25-55"><a href="#cb25-55" aria-hidden="true" tabindex="-1"></a>  <span class="at">coef_map =</span> coefname,</span>
<span id="cb25-56"><a href="#cb25-56" aria-hidden="true" tabindex="-1"></a>  <span class="at">add_rows =</span> rows,</span>
<span id="cb25-57"><a href="#cb25-57" aria-hidden="true" tabindex="-1"></a>  <span class="at">gof_omit =</span> <span class="st">&quot;ICC|RMSE&quot;</span>,</span>
<span id="cb25-58"><a href="#cb25-58" aria-hidden="true" tabindex="-1"></a>  <span class="at">output =</span> <span class="st">&quot;gt&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
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<table class="gt_table caption-top table table-sm table-striped small" data-quarto-postprocess="true" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
<thead>
<tr class="header gt_col_headings">
<th id="a-" class="gt_col_heading gt_columns_bottom_border gt_left" data-quarto-table-cell-role="th" scope="col"></th>
<th id="Model-1" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col">Model 1</th>
<th id="Model-2" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col">Model 2</th>
<th id="Model-3" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col">Model 3</th>
<th id="Model-4" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col">Model 4</th>
</tr>
</thead>
<tbody class="gt_table_body">
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Interwar Generation</td>
<td class="gt_row gt_center" headers="Model 1">ref.</td>
<td class="gt_row gt_center" headers="Model 2">ref.</td>
<td class="gt_row gt_center" headers="Model 3">ref.</td>
<td class="gt_row gt_center" headers="Model 4">ref.</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Post-War Generation</td>
<td class="gt_row gt_center" headers="Model 1">0.80***</td>
<td class="gt_row gt_center" headers="Model 2">0.65***</td>
<td class="gt_row gt_center" headers="Model 3">0.35</td>
<td class="gt_row gt_center" headers="Model 4">0.33</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1">(0.17)</td>
<td class="gt_row gt_center" headers="Model 2">(0.17)</td>
<td class="gt_row gt_center" headers="Model 3">(0.24)</td>
<td class="gt_row gt_center" headers="Model 4">(0.48)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">60-70s Generation</td>
<td class="gt_row gt_center" headers="Model 1">1.33***</td>
<td class="gt_row gt_center" headers="Model 2">1.08***</td>
<td class="gt_row gt_center" headers="Model 3">0.95***</td>
<td class="gt_row gt_center" headers="Model 4">1.26*</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1">(0.17)</td>
<td class="gt_row gt_center" headers="Model 2">(0.17)</td>
<td class="gt_row gt_center" headers="Model 3">(0.27)</td>
<td class="gt_row gt_center" headers="Model 4">(0.52)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">80s Generation</td>
<td class="gt_row gt_center" headers="Model 1">1.71***</td>
<td class="gt_row gt_center" headers="Model 2">1.39***</td>
<td class="gt_row gt_center" headers="Model 3">1.24***</td>
<td class="gt_row gt_center" headers="Model 4">1.58**</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1">(0.17)</td>
<td class="gt_row gt_center" headers="Model 2">(0.17)</td>
<td class="gt_row gt_center" headers="Model 3">(0.29)</td>
<td class="gt_row gt_center" headers="Model 4">(0.55)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">90s Generation</td>
<td class="gt_row gt_center" headers="Model 1">1.84***</td>
<td class="gt_row gt_center" headers="Model 2">1.44***</td>
<td class="gt_row gt_center" headers="Model 3">1.35***</td>
<td class="gt_row gt_center" headers="Model 4">1.98***</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1">(0.17)</td>
<td class="gt_row gt_center" headers="Model 2">(0.17)</td>
<td class="gt_row gt_center" headers="Model 3">(0.30)</td>
<td class="gt_row gt_center" headers="Model 4">(0.56)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Millennials</td>
<td class="gt_row gt_center" headers="Model 1">2.00***</td>
<td class="gt_row gt_center" headers="Model 2">1.58***</td>
<td class="gt_row gt_center" headers="Model 3">1.44***</td>
<td class="gt_row gt_center" headers="Model 4">1.87**</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1">(0.18)</td>
<td class="gt_row gt_center" headers="Model 2">(0.18)</td>
<td class="gt_row gt_center" headers="Model 3">(0.37)</td>
<td class="gt_row gt_center" headers="Model 4">(0.66)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Gender: male</td>
<td class="gt_row gt_center" headers="Model 1">ref.</td>
<td class="gt_row gt_center" headers="Model 2">ref.</td>
<td class="gt_row gt_center" headers="Model 3">ref.</td>
<td class="gt_row gt_center" headers="Model 4">ref.</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Gender: female</td>
<td class="gt_row gt_center" headers="Model 1">0.44***</td>
<td class="gt_row gt_center" headers="Model 2">0.48***</td>
<td class="gt_row gt_center" headers="Model 3">0.48***</td>
<td class="gt_row gt_center" headers="Model 4">0.73</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1">(0.02)</td>
<td class="gt_row gt_center" headers="Model 2">(0.02)</td>
<td class="gt_row gt_center" headers="Model 3">(0.02)</td>
<td class="gt_row gt_center" headers="Model 4">(0.81)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Age: 22-29</td>
<td class="gt_row gt_center" headers="Model 1">ref.</td>
<td class="gt_row gt_center" headers="Model 2">ref.</td>
<td class="gt_row gt_center" headers="Model 3">ref.</td>
<td class="gt_row gt_center" headers="Model 4">ref.</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Age: 30-65</td>
<td class="gt_row gt_center" headers="Model 1">-0.08+</td>
<td class="gt_row gt_center" headers="Model 2">-0.10*</td>
<td class="gt_row gt_center" headers="Model 3">-0.26</td>
<td class="gt_row gt_center" headers="Model 4">-0.45</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1">(0.05)</td>
<td class="gt_row gt_center" headers="Model 2">(0.05)</td>
<td class="gt_row gt_center" headers="Model 3">(0.25)</td>
<td class="gt_row gt_center" headers="Model 4">(0.36)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Age: 66+</td>
<td class="gt_row gt_center" headers="Model 1">-0.39***</td>
<td class="gt_row gt_center" headers="Model 2">-0.36***</td>
<td class="gt_row gt_center" headers="Model 3">-0.50+</td>
<td class="gt_row gt_center" headers="Model 4">-0.26</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1">(0.07)</td>
<td class="gt_row gt_center" headers="Model 2">(0.07)</td>
<td class="gt_row gt_center" headers="Model 3">(0.30)</td>
<td class="gt_row gt_center" headers="Model 4">(0.46)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Immigrant background</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2">0.11***</td>
<td class="gt_row gt_center" headers="Model 3">0.10***</td>
<td class="gt_row gt_center" headers="Model 4">0.10***</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2">(0.03)</td>
<td class="gt_row gt_center" headers="Model 3">(0.03)</td>
<td class="gt_row gt_center" headers="Model 4">(0.03)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Education: (Below) lower secondary</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2">ref.</td>
<td class="gt_row gt_center" headers="Model 3">ref.</td>
<td class="gt_row gt_center" headers="Model 4">ref.</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Education: upper secondary</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2">0.42***</td>
<td class="gt_row gt_center" headers="Model 3">-0.26</td>
<td class="gt_row gt_center" headers="Model 4">0.44</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2">(0.04)</td>
<td class="gt_row gt_center" headers="Model 3">(0.52)</td>
<td class="gt_row gt_center" headers="Model 4">(0.82)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Education: tertiary</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2">1.13***</td>
<td class="gt_row gt_center" headers="Model 3">0.49</td>
<td class="gt_row gt_center" headers="Model 4">0.73</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2">(0.04)</td>
<td class="gt_row gt_center" headers="Model 3">(0.50)</td>
<td class="gt_row gt_center" headers="Model 4">(0.81)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Generation X Education</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4"></td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Post-War Generation # education: upper secondary</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3">0.74+</td>
<td class="gt_row gt_center" headers="Model 4">0.23</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3">(0.42)</td>
<td class="gt_row gt_center" headers="Model 4">(0.66)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Post-War Generation # education: tertiary</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3">0.31</td>
<td class="gt_row gt_center" headers="Model 4">0.28</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3">(0.41)</td>
<td class="gt_row gt_center" headers="Model 4">(0.66)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">60-70s Generation # education: upper secondary</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3">0.39</td>
<td class="gt_row gt_center" headers="Model 4">-0.37</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3">(0.44)</td>
<td class="gt_row gt_center" headers="Model 4">(0.70)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">60-70s Generation # education: tertiary</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3">0.22</td>
<td class="gt_row gt_center" headers="Model 4">-0.08</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3">(0.43)</td>
<td class="gt_row gt_center" headers="Model 4">(0.70)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">80s Generation # education: upper secondary</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3">0.40</td>
<td class="gt_row gt_center" headers="Model 4">-0.41</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3">(0.45)</td>
<td class="gt_row gt_center" headers="Model 4">(0.73)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">80s Generation # education: tertiary</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3">0.27</td>
<td class="gt_row gt_center" headers="Model 4">0.02</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3">(0.44)</td>
<td class="gt_row gt_center" headers="Model 4">(0.72)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Millennials # education: upper secondary</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3">0.45</td>
<td class="gt_row gt_center" headers="Model 4">-0.31</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3">(0.51)</td>
<td class="gt_row gt_center" headers="Model 4">(0.82)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Millennials # education: tertiary</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3">0.21</td>
<td class="gt_row gt_center" headers="Model 4">-0.22</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3">(0.50)</td>
<td class="gt_row gt_center" headers="Model 4">(0.81)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Generation X Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4"></td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Post-War Generation # Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">0.01</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">(0.55)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">60-70s Generation # Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">-0.47</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">(0.61)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">80s Generation # Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">-0.49</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">(0.64)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">90s Generation # Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">-0.98</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">(0.66)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Millennials # Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">-0.60</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">(0.79)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Education X Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4"></td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Education: upper secondary # Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">-0.96</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">(1.09)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Education: tertiary # female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">-0.14</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">(1.06)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Generation X Education X Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4"></td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Post-War Generation # Educ.: upper secondary # Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">0.93</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">(0.88)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">60-70s Generation # Educ.: upper secondary # Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">1.28</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">(0.93)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">80s Generation # Educ.: upper secondary # Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">1.33</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">(0.96)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">90s Generation # Educ.: upper secondary # Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">1.60+</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">(0.97)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Millennials # Educ.: upper secondary # Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">1.22</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">(1.08)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Post-War Generation # Educ.: tertiary # Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">0.10</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">(0.86)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">60-70s Generation # Educ.: tertiary # Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">0.44</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">(0.90)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">80s Generation # Educ.: tertiary # Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">0.33</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">(0.92)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">90s Generation # Educ.: tertiary # Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">0.91</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">(0.94)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Millennials # Educ.: tertiary # Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">0.60</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" " style="border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #000000"></td>
<td class="gt_row gt_center" headers="Model 1" style="border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #000000"></td>
<td class="gt_row gt_center" headers="Model 2" style="border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #000000"></td>
<td class="gt_row gt_center" headers="Model 3" style="border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #000000"></td>
<td class="gt_row gt_center" headers="Model 4" style="border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #000000">(1.04)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Country fixed effects</td>
<td class="gt_row gt_center" headers="Model 1">yes</td>
<td class="gt_row gt_center" headers="Model 2">yes</td>
<td class="gt_row gt_center" headers="Model 3">yes</td>
<td class="gt_row gt_center" headers="Model 4">yes</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Twoway interaction Age group X Education</td>
<td class="gt_row gt_center" headers="Model 1">no</td>
<td class="gt_row gt_center" headers="Model 2">no</td>
<td class="gt_row gt_center" headers="Model 3">yes</td>
<td class="gt_row gt_center" headers="Model 4">yes</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Twoway interaction ESS wave X Education</td>
<td class="gt_row gt_center" headers="Model 1">no</td>
<td class="gt_row gt_center" headers="Model 2">no</td>
<td class="gt_row gt_center" headers="Model 3">yes</td>
<td class="gt_row gt_center" headers="Model 4">yes</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Threeway interaction Age group X Education X Gender</td>
<td class="gt_row gt_center" headers="Model 1">no</td>
<td class="gt_row gt_center" headers="Model 2">no</td>
<td class="gt_row gt_center" headers="Model 3">no</td>
<td class="gt_row gt_center" headers="Model 4">yes</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Threeway interaction ESS wave X Education X Gender</td>
<td class="gt_row gt_center" headers="Model 1">no</td>
<td class="gt_row gt_center" headers="Model 2">no</td>
<td class="gt_row gt_center" headers="Model 3">no</td>
<td class="gt_row gt_center" headers="Model 4">yes</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Num.Obs.</td>
<td class="gt_row gt_center" headers="Model 1">125356</td>
<td class="gt_row gt_center" headers="Model 2">124209</td>
<td class="gt_row gt_center" headers="Model 3">124209</td>
<td class="gt_row gt_center" headers="Model 4">124209</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">R2 Marg.</td>
<td class="gt_row gt_center" headers="Model 1">0.288</td>
<td class="gt_row gt_center" headers="Model 2">0.324</td>
<td class="gt_row gt_center" headers="Model 3">0.326</td>
<td class="gt_row gt_center" headers="Model 4">0.330</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">R2 Cond.</td>
<td class="gt_row gt_center" headers="Model 1">0.323</td>
<td class="gt_row gt_center" headers="Model 2">0.358</td>
<td class="gt_row gt_center" headers="Model 3">0.359</td>
<td class="gt_row gt_center" headers="Model 4">0.362</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">AIC</td>
<td class="gt_row gt_center" headers="Model 1">61185.6</td>
<td class="gt_row gt_center" headers="Model 2">59349.7</td>
<td class="gt_row gt_center" headers="Model 3">59364.7</td>
<td class="gt_row gt_center" headers="Model 4">59383.7</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">BIC</td>
<td class="gt_row gt_center" headers="Model 1">61458.3</td>
<td class="gt_row gt_center" headers="Model 2">59651.3</td>
<td class="gt_row gt_center" headers="Model 3">59977.7</td>
<td class="gt_row gt_center" headers="Model 4">60483.2</td>
</tr>
</tbody><tfoot class="gt_sourcenotes">
<tr class="odd">
<td colspan="5" class="gt_sourcenote">+ p &lt; 0.1, * p &lt; 0.05, ** p &lt; 0.01, *** p &lt; 0.001</td>
</tr>
</tfoot>

</table>

</div>
<p>Voting for green parties – regression coefficients</p>
</div>
</div>
</section>
<section id="all-models-of-radical-right-vote" class="level2" data-number="5.3">
<h2 data-number="5.3" class="anchored" data-anchor-id="all-models-of-radical-right-vote"><span class="header-section-number">5.3</span> All models of radical right vote</h2>
<div id="tbl-table_b2" class="cell anchored">
<details class="code-fold">
<summary>Show the code</summary>
<div class="sourceCode cell-code" id="cb26"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a>models <span class="ot">&lt;-</span> <span class="fu">list</span>(</span>
<span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Model 1&quot;</span> <span class="ot">=</span> <span class="fu">glmer</span>(</span>
<span id="cb26-3"><a href="#cb26-3" aria-hidden="true" tabindex="-1"></a>    vote_radright <span class="sc">~</span> cohort_fct <span class="sc">+</span> gndr_fct <span class="sc">+</span></span>
<span id="cb26-4"><a href="#cb26-4" aria-hidden="true" tabindex="-1"></a>      <span class="fu">factor</span>(age_group) <span class="sc">+</span></span>
<span id="cb26-5"><a href="#cb26-5" aria-hidden="true" tabindex="-1"></a>      <span class="fu">factor</span>(essround) <span class="sc">+</span> <span class="fu">factor</span>(cntry_id) <span class="sc">+</span></span>
<span id="cb26-6"><a href="#cb26-6" aria-hidden="true" tabindex="-1"></a>      (<span class="dv">1</span> <span class="sc">|</span> cntry_wave_id),</span>
<span id="cb26-7"><a href="#cb26-7" aria-hidden="true" tabindex="-1"></a>    <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">&quot;logit&quot;</span>),</span>
<span id="cb26-8"><a href="#cb26-8" aria-hidden="true" tabindex="-1"></a>    <span class="at">nAGQ =</span> <span class="dv">0</span>,</span>
<span id="cb26-9"><a href="#cb26-9" aria-hidden="true" tabindex="-1"></a>    <span class="at">data =</span> dfradicalright</span>
<span id="cb26-10"><a href="#cb26-10" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb26-11"><a href="#cb26-11" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Model 2&quot;</span> <span class="ot">=</span> <span class="fu">glmer</span>(</span>
<span id="cb26-12"><a href="#cb26-12" aria-hidden="true" tabindex="-1"></a>    vote_radright <span class="sc">~</span> cohort_fct <span class="sc">+</span> gndr_fct <span class="sc">+</span> edu_fct <span class="sc">+</span></span>
<span id="cb26-13"><a href="#cb26-13" aria-hidden="true" tabindex="-1"></a>      <span class="fu">factor</span>(age_group) <span class="sc">+</span> immigrant <span class="sc">+</span></span>
<span id="cb26-14"><a href="#cb26-14" aria-hidden="true" tabindex="-1"></a>      <span class="fu">factor</span>(essround) <span class="sc">+</span> <span class="fu">factor</span>(cntry_id) <span class="sc">+</span></span>
<span id="cb26-15"><a href="#cb26-15" aria-hidden="true" tabindex="-1"></a>      (<span class="dv">1</span> <span class="sc">|</span> cntry_wave_id),</span>
<span id="cb26-16"><a href="#cb26-16" aria-hidden="true" tabindex="-1"></a>    <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">&quot;logit&quot;</span>),</span>
<span id="cb26-17"><a href="#cb26-17" aria-hidden="true" tabindex="-1"></a>    <span class="at">nAGQ =</span> <span class="dv">0</span>,</span>
<span id="cb26-18"><a href="#cb26-18" aria-hidden="true" tabindex="-1"></a>    <span class="at">data =</span> dfradicalright</span>
<span id="cb26-19"><a href="#cb26-19" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb26-20"><a href="#cb26-20" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Model 3&quot;</span> <span class="ot">=</span> <span class="fu">glmer</span>(</span>
<span id="cb26-21"><a href="#cb26-21" aria-hidden="true" tabindex="-1"></a>    vote_radright <span class="sc">~</span> cohort_fct <span class="sc">*</span> edu_fct <span class="sc">+</span></span>
<span id="cb26-22"><a href="#cb26-22" aria-hidden="true" tabindex="-1"></a>      <span class="fu">factor</span>(age_group) <span class="sc">*</span> edu_fct <span class="sc">+</span></span>
<span id="cb26-23"><a href="#cb26-23" aria-hidden="true" tabindex="-1"></a>      <span class="fu">factor</span>(essround) <span class="sc">*</span> edu_fct <span class="sc">+</span></span>
<span id="cb26-24"><a href="#cb26-24" aria-hidden="true" tabindex="-1"></a>      gndr_fct <span class="sc">+</span> immigrant <span class="sc">+</span></span>
<span id="cb26-25"><a href="#cb26-25" aria-hidden="true" tabindex="-1"></a>      <span class="sc">+</span><span class="fu">factor</span>(cntry_id) <span class="sc">+</span></span>
<span id="cb26-26"><a href="#cb26-26" aria-hidden="true" tabindex="-1"></a>      (<span class="dv">1</span> <span class="sc">|</span> cntry_wave_id),</span>
<span id="cb26-27"><a href="#cb26-27" aria-hidden="true" tabindex="-1"></a>    <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">&quot;logit&quot;</span>),</span>
<span id="cb26-28"><a href="#cb26-28" aria-hidden="true" tabindex="-1"></a>    <span class="at">nAGQ =</span> <span class="dv">0</span>,</span>
<span id="cb26-29"><a href="#cb26-29" aria-hidden="true" tabindex="-1"></a>    <span class="at">data =</span> dfradicalright</span>
<span id="cb26-30"><a href="#cb26-30" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb26-31"><a href="#cb26-31" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Model 4&quot;</span> <span class="ot">=</span> <span class="fu">glmer</span>(</span>
<span id="cb26-32"><a href="#cb26-32" aria-hidden="true" tabindex="-1"></a>    vote_radright <span class="sc">~</span> cohort_fct <span class="sc">*</span> edu_fct <span class="sc">*</span> gndr_fct <span class="sc">+</span></span>
<span id="cb26-33"><a href="#cb26-33" aria-hidden="true" tabindex="-1"></a>      <span class="fu">factor</span>(age_group) <span class="sc">*</span> edu_fct <span class="sc">*</span> gndr_fct <span class="sc">+</span></span>
<span id="cb26-34"><a href="#cb26-34" aria-hidden="true" tabindex="-1"></a>      <span class="fu">factor</span>(essround) <span class="sc">*</span> edu_fct <span class="sc">*</span> gndr_fct <span class="sc">+</span></span>
<span id="cb26-35"><a href="#cb26-35" aria-hidden="true" tabindex="-1"></a>      immigrant <span class="sc">+</span></span>
<span id="cb26-36"><a href="#cb26-36" aria-hidden="true" tabindex="-1"></a>      <span class="fu">factor</span>(cntry_id) <span class="sc">+</span> (<span class="dv">1</span> <span class="sc">|</span> cntry_wave_id),</span>
<span id="cb26-37"><a href="#cb26-37" aria-hidden="true" tabindex="-1"></a>    <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">&quot;logit&quot;</span>),</span>
<span id="cb26-38"><a href="#cb26-38" aria-hidden="true" tabindex="-1"></a>    <span class="at">nAGQ =</span> <span class="dv">0</span>,</span>
<span id="cb26-39"><a href="#cb26-39" aria-hidden="true" tabindex="-1"></a>    <span class="at">data =</span> dfradicalright</span>
<span id="cb26-40"><a href="#cb26-40" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb26-41"><a href="#cb26-41" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb26-42"><a href="#cb26-42" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-43"><a href="#cb26-43" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-44"><a href="#cb26-44" aria-hidden="true" tabindex="-1"></a><span class="co"># create the table</span></span>
<span id="cb26-45"><a href="#cb26-45" aria-hidden="true" tabindex="-1"></a><span class="fu">modelsummary</span>(models,</span>
<span id="cb26-46"><a href="#cb26-46" aria-hidden="true" tabindex="-1"></a>  <span class="at">stars =</span> <span class="cn">TRUE</span>, <span class="at">fmt =</span> <span class="dv">2</span>,</span>
<span id="cb26-47"><a href="#cb26-47" aria-hidden="true" tabindex="-1"></a>  <span class="at">coef_map =</span> coefname,</span>
<span id="cb26-48"><a href="#cb26-48" aria-hidden="true" tabindex="-1"></a>  <span class="at">add_rows =</span> rows,</span>
<span id="cb26-49"><a href="#cb26-49" aria-hidden="true" tabindex="-1"></a>  <span class="at">gof_omit =</span> <span class="st">&quot;ICC|RMSE&quot;</span>,</span>
<span id="cb26-50"><a href="#cb26-50" aria-hidden="true" tabindex="-1"></a>  <span class="at">output =</span> <span class="fu">here</span>(<span class="st">&quot;tables&quot;</span>, <span class="st">&quot;appendix_table_c2.docx&quot;</span>)</span>
<span id="cb26-51"><a href="#cb26-51" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb26-52"><a href="#cb26-52" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb26-53"><a href="#cb26-53" aria-hidden="true" tabindex="-1"></a><span class="fu">modelsummary</span>(models,</span>
<span id="cb26-54"><a href="#cb26-54" aria-hidden="true" tabindex="-1"></a>  <span class="at">stars =</span> <span class="cn">TRUE</span>, <span class="at">fmt =</span> <span class="dv">2</span>,</span>
<span id="cb26-55"><a href="#cb26-55" aria-hidden="true" tabindex="-1"></a>  <span class="at">coef_map =</span> coefname,</span>
<span id="cb26-56"><a href="#cb26-56" aria-hidden="true" tabindex="-1"></a>  <span class="at">add_rows =</span> rows,</span>
<span id="cb26-57"><a href="#cb26-57" aria-hidden="true" tabindex="-1"></a>  <span class="at">gof_omit =</span> <span class="st">&quot;ICC|RMSE&quot;</span>,</span>
<span id="cb26-58"><a href="#cb26-58" aria-hidden="true" tabindex="-1"></a>  <span class="at">output =</span> <span class="st">&quot;gt&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
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padding-left: 5px;
padding-right: 5px;
}
#xuilsfhwrh .gt_stub_row_group {
color: #333333;
background-color: #FFFFFF;
font-size: 100%;
font-weight: initial;
text-transform: inherit;
border-right-style: solid;
border-right-width: 2px;
border-right-color: #D3D3D3;
padding-left: 5px;
padding-right: 5px;
vertical-align: top;
}
#xuilsfhwrh .gt_row_group_first td {
border-top-width: 2px;
}
#xuilsfhwrh .gt_row_group_first th {
border-top-width: 2px;
}
#xuilsfhwrh .gt_summary_row {
color: #333333;
background-color: #FFFFFF;
text-transform: inherit;
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
}
#xuilsfhwrh .gt_first_summary_row {
border-top-style: solid;
border-top-color: #D3D3D3;
}
#xuilsfhwrh .gt_first_summary_row.thick {
border-top-width: 2px;
}
#xuilsfhwrh .gt_last_summary_row {
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
border-bottom-style: solid;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
}
#xuilsfhwrh .gt_grand_summary_row {
color: #333333;
background-color: #FFFFFF;
text-transform: inherit;
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
}
#xuilsfhwrh .gt_first_grand_summary_row {
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
border-top-style: double;
border-top-width: 6px;
border-top-color: #D3D3D3;
}
#xuilsfhwrh .gt_last_grand_summary_row_top {
padding-top: 8px;
padding-bottom: 8px;
padding-left: 5px;
padding-right: 5px;
border-bottom-style: double;
border-bottom-width: 6px;
border-bottom-color: #D3D3D3;
}
#xuilsfhwrh .gt_striped {
background-color: rgba(128, 128, 128, 0.05);
}
#xuilsfhwrh .gt_table_body {
border-top-style: solid;
border-top-width: 2px;
border-top-color: #D3D3D3;
border-bottom-style: solid;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
}
#xuilsfhwrh .gt_footnotes {
color: #333333;
background-color: #FFFFFF;
border-bottom-style: none;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
border-left-style: none;
border-left-width: 2px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 2px;
border-right-color: #D3D3D3;
}
#xuilsfhwrh .gt_footnote {
margin: 0px;
font-size: 90%;
padding-top: 4px;
padding-bottom: 4px;
padding-left: 5px;
padding-right: 5px;
}
#xuilsfhwrh .gt_sourcenotes {
color: #333333;
background-color: #FFFFFF;
border-bottom-style: none;
border-bottom-width: 2px;
border-bottom-color: #D3D3D3;
border-left-style: none;
border-left-width: 2px;
border-left-color: #D3D3D3;
border-right-style: none;
border-right-width: 2px;
border-right-color: #D3D3D3;
}
#xuilsfhwrh .gt_sourcenote {
font-size: 90%;
padding-top: 4px;
padding-bottom: 4px;
padding-left: 5px;
padding-right: 5px;
}
#xuilsfhwrh .gt_left {
text-align: left;
}
#xuilsfhwrh .gt_center {
text-align: center;
}
#xuilsfhwrh .gt_right {
text-align: right;
font-variant-numeric: tabular-nums;
}
#xuilsfhwrh .gt_font_normal {
font-weight: normal;
}
#xuilsfhwrh .gt_font_bold {
font-weight: bold;
}
#xuilsfhwrh .gt_font_italic {
font-style: italic;
}
#xuilsfhwrh .gt_super {
font-size: 65%;
}
#xuilsfhwrh .gt_footnote_marks {
font-size: 75%;
vertical-align: 0.4em;
position: initial;
}
#xuilsfhwrh .gt_asterisk {
font-size: 100%;
vertical-align: 0;
}
#xuilsfhwrh .gt_indent_1 {
text-indent: 5px;
}
#xuilsfhwrh .gt_indent_2 {
text-indent: 10px;
}
#xuilsfhwrh .gt_indent_3 {
text-indent: 15px;
}
#xuilsfhwrh .gt_indent_4 {
text-indent: 20px;
}
#xuilsfhwrh .gt_indent_5 {
text-indent: 25px;
}
#xuilsfhwrh .katex-display {
display: inline-flex !important;
margin-bottom: 0.75em !important;
}
#xuilsfhwrh div.Reactable > div.rt-table > div.rt-thead > div.rt-tr.rt-tr-group-header > div.rt-th-group:after {
height: 0px !important;
}
</style>

<table class="gt_table caption-top table table-sm table-striped small" data-quarto-postprocess="true" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
<thead>
<tr class="header gt_col_headings">
<th id="a-" class="gt_col_heading gt_columns_bottom_border gt_left" data-quarto-table-cell-role="th" scope="col"></th>
<th id="Model-1" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col">Model 1</th>
<th id="Model-2" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col">Model 2</th>
<th id="Model-3" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col">Model 3</th>
<th id="Model-4" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col">Model 4</th>
</tr>
</thead>
<tbody class="gt_table_body">
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Interwar Generation</td>
<td class="gt_row gt_center" headers="Model 1">ref.</td>
<td class="gt_row gt_center" headers="Model 2">ref.</td>
<td class="gt_row gt_center" headers="Model 3">ref.</td>
<td class="gt_row gt_center" headers="Model 4">ref.</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Post-War Generation</td>
<td class="gt_row gt_center" headers="Model 1">0.24*</td>
<td class="gt_row gt_center" headers="Model 2">0.34***</td>
<td class="gt_row gt_center" headers="Model 3">0.49***</td>
<td class="gt_row gt_center" headers="Model 4">0.20</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1">(0.10)</td>
<td class="gt_row gt_center" headers="Model 2">(0.10)</td>
<td class="gt_row gt_center" headers="Model 3">(0.14)</td>
<td class="gt_row gt_center" headers="Model 4">(0.19)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">60-70s Generation</td>
<td class="gt_row gt_center" headers="Model 1">0.28**</td>
<td class="gt_row gt_center" headers="Model 2">0.49***</td>
<td class="gt_row gt_center" headers="Model 3">0.72***</td>
<td class="gt_row gt_center" headers="Model 4">0.33</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1">(0.10)</td>
<td class="gt_row gt_center" headers="Model 2">(0.10)</td>
<td class="gt_row gt_center" headers="Model 3">(0.15)</td>
<td class="gt_row gt_center" headers="Model 4">(0.21)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">80s Generation</td>
<td class="gt_row gt_center" headers="Model 1">0.37***</td>
<td class="gt_row gt_center" headers="Model 2">0.68***</td>
<td class="gt_row gt_center" headers="Model 3">0.97***</td>
<td class="gt_row gt_center" headers="Model 4">0.66**</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1">(0.11)</td>
<td class="gt_row gt_center" headers="Model 2">(0.11)</td>
<td class="gt_row gt_center" headers="Model 3">(0.17)</td>
<td class="gt_row gt_center" headers="Model 4">(0.23)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">90s Generation</td>
<td class="gt_row gt_center" headers="Model 1">0.38***</td>
<td class="gt_row gt_center" headers="Model 2">0.83***</td>
<td class="gt_row gt_center" headers="Model 3">1.24***</td>
<td class="gt_row gt_center" headers="Model 4">0.69**</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1">(0.11)</td>
<td class="gt_row gt_center" headers="Model 2">(0.11)</td>
<td class="gt_row gt_center" headers="Model 3">(0.18)</td>
<td class="gt_row gt_center" headers="Model 4">(0.24)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Millennials</td>
<td class="gt_row gt_center" headers="Model 1">0.35**</td>
<td class="gt_row gt_center" headers="Model 2">0.83***</td>
<td class="gt_row gt_center" headers="Model 3">1.31***</td>
<td class="gt_row gt_center" headers="Model 4">0.73*</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1">(0.12)</td>
<td class="gt_row gt_center" headers="Model 2">(0.12)</td>
<td class="gt_row gt_center" headers="Model 3">(0.22)</td>
<td class="gt_row gt_center" headers="Model 4">(0.31)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Gender: male</td>
<td class="gt_row gt_center" headers="Model 1">ref.</td>
<td class="gt_row gt_center" headers="Model 2">ref.</td>
<td class="gt_row gt_center" headers="Model 3">ref.</td>
<td class="gt_row gt_center" headers="Model 4">ref.</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Gender: female</td>
<td class="gt_row gt_center" headers="Model 1">-0.45***</td>
<td class="gt_row gt_center" headers="Model 2">-0.50***</td>
<td class="gt_row gt_center" headers="Model 3">-0.48***</td>
<td class="gt_row gt_center" headers="Model 4">-0.99*</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1">(0.02)</td>
<td class="gt_row gt_center" headers="Model 2">(0.02)</td>
<td class="gt_row gt_center" headers="Model 3">(0.02)</td>
<td class="gt_row gt_center" headers="Model 4">(0.49)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Age: 22-29</td>
<td class="gt_row gt_center" headers="Model 1">ref.</td>
<td class="gt_row gt_center" headers="Model 2">ref.</td>
<td class="gt_row gt_center" headers="Model 3">ref.</td>
<td class="gt_row gt_center" headers="Model 4">ref.</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Age: 30-65</td>
<td class="gt_row gt_center" headers="Model 1">-0.03</td>
<td class="gt_row gt_center" headers="Model 2">0.02</td>
<td class="gt_row gt_center" headers="Model 3">0.10</td>
<td class="gt_row gt_center" headers="Model 4">0.06</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1">(0.06)</td>
<td class="gt_row gt_center" headers="Model 2">(0.06)</td>
<td class="gt_row gt_center" headers="Model 3">(0.19)</td>
<td class="gt_row gt_center" headers="Model 4">(0.26)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Age: 66+</td>
<td class="gt_row gt_center" headers="Model 1">-0.02</td>
<td class="gt_row gt_center" headers="Model 2">-0.06</td>
<td class="gt_row gt_center" headers="Model 3">-0.08</td>
<td class="gt_row gt_center" headers="Model 4">-0.15</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1">(0.08)</td>
<td class="gt_row gt_center" headers="Model 2">(0.08)</td>
<td class="gt_row gt_center" headers="Model 3">(0.21)</td>
<td class="gt_row gt_center" headers="Model 4">(0.28)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Immigrant background</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2">-0.21***</td>
<td class="gt_row gt_center" headers="Model 3">-0.23***</td>
<td class="gt_row gt_center" headers="Model 4">-0.23***</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2">(0.04)</td>
<td class="gt_row gt_center" headers="Model 3">(0.04)</td>
<td class="gt_row gt_center" headers="Model 4">(0.04)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Education: (Below) lower secondary</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2">ref.</td>
<td class="gt_row gt_center" headers="Model 3">ref.</td>
<td class="gt_row gt_center" headers="Model 4">ref.</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Education: upper secondary</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2">-0.37***</td>
<td class="gt_row gt_center" headers="Model 3">-0.17</td>
<td class="gt_row gt_center" headers="Model 4">-0.36</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2">(0.03)</td>
<td class="gt_row gt_center" headers="Model 3">(0.32)</td>
<td class="gt_row gt_center" headers="Model 4">(0.43)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Education: tertiary</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2">-1.49***</td>
<td class="gt_row gt_center" headers="Model 3">0.07</td>
<td class="gt_row gt_center" headers="Model 4">-0.20</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2">(0.04)</td>
<td class="gt_row gt_center" headers="Model 3">(0.37)</td>
<td class="gt_row gt_center" headers="Model 4">(0.47)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Generation X Education</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4"></td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Post-War Generation # education: upper secondary</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3">-0.29</td>
<td class="gt_row gt_center" headers="Model 4">-0.04</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3">(0.22)</td>
<td class="gt_row gt_center" headers="Model 4">(0.30)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Post-War Generation # education: tertiary</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3">-0.95***</td>
<td class="gt_row gt_center" headers="Model 4">-0.70*</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3">(0.27)</td>
<td class="gt_row gt_center" headers="Model 4">(0.33)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">60-70s Generation # education: upper secondary</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3">-0.35</td>
<td class="gt_row gt_center" headers="Model 4">-0.03</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3">(0.24)</td>
<td class="gt_row gt_center" headers="Model 4">(0.32)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">60-70s Generation # education: tertiary</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3">-1.44***</td>
<td class="gt_row gt_center" headers="Model 4">-1.02**</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3">(0.29)</td>
<td class="gt_row gt_center" headers="Model 4">(0.35)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">80s Generation # education: upper secondary</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3">-0.32</td>
<td class="gt_row gt_center" headers="Model 4">-0.11</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3">(0.25)</td>
<td class="gt_row gt_center" headers="Model 4">(0.34)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">80s Generation # education: tertiary</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3">-1.67***</td>
<td class="gt_row gt_center" headers="Model 4">-1.30***</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3">(0.31)</td>
<td class="gt_row gt_center" headers="Model 4">(0.38)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Millennials # education: upper secondary</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3">-0.39</td>
<td class="gt_row gt_center" headers="Model 4">0.01</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3">(0.30)</td>
<td class="gt_row gt_center" headers="Model 4">(0.40)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Millennials # education: tertiary</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3">-2.06***</td>
<td class="gt_row gt_center" headers="Model 4">-1.39**</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3">(0.35)</td>
<td class="gt_row gt_center" headers="Model 4">(0.44)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Generation X Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4"></td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Post-War Generation # Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">0.57*</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">(0.27)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">60-70s Generation # Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">0.77*</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">(0.31)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">80s Generation # Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">0.62+</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">(0.33)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">90s Generation # Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">1.13**</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">(0.35)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Millennials # Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">1.21**</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">(0.44)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Education X Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4"></td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Education: upper secondary # Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">0.29</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">(0.64)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Education: tertiary # female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">0.41</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">(0.81)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Generation X Education X Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4"></td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Post-War Generation # Educ.: upper secondary # Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">-0.49</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">(0.45)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">60-70s Generation # Educ.: upper secondary # Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">-0.61</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">(0.48)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">80s Generation # Educ.: upper secondary # Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">-0.37</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">(0.51)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">90s Generation # Educ.: upper secondary # Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">-0.90+</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">(0.52)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Millennials # Educ.: upper secondary # Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">-0.75</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">(0.60)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Post-War Generation # Educ.: tertiary # Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">-0.37</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">(0.62)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">60-70s Generation # Educ.: tertiary # Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">-0.79</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">(0.66)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">80s Generation # Educ.: tertiary # Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">-0.72</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">(0.69)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">90s Generation # Educ.: tertiary # Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">-1.10</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">(0.70)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Millennials # Educ.: tertiary # Female</td>
<td class="gt_row gt_center" headers="Model 1"></td>
<td class="gt_row gt_center" headers="Model 2"></td>
<td class="gt_row gt_center" headers="Model 3"></td>
<td class="gt_row gt_center" headers="Model 4">-1.35+</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" " style="border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #000000"></td>
<td class="gt_row gt_center" headers="Model 1" style="border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #000000"></td>
<td class="gt_row gt_center" headers="Model 2" style="border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #000000"></td>
<td class="gt_row gt_center" headers="Model 3" style="border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #000000"></td>
<td class="gt_row gt_center" headers="Model 4" style="border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #000000">(0.77)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Country fixed effects</td>
<td class="gt_row gt_center" headers="Model 1">yes</td>
<td class="gt_row gt_center" headers="Model 2">yes</td>
<td class="gt_row gt_center" headers="Model 3">yes</td>
<td class="gt_row gt_center" headers="Model 4">yes</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Twoway interaction Age group X Education</td>
<td class="gt_row gt_center" headers="Model 1">no</td>
<td class="gt_row gt_center" headers="Model 2">no</td>
<td class="gt_row gt_center" headers="Model 3">yes</td>
<td class="gt_row gt_center" headers="Model 4">yes</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Twoway interaction ESS wave X Education</td>
<td class="gt_row gt_center" headers="Model 1">no</td>
<td class="gt_row gt_center" headers="Model 2">no</td>
<td class="gt_row gt_center" headers="Model 3">yes</td>
<td class="gt_row gt_center" headers="Model 4">yes</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Threeway interaction Age group X Education X Gender</td>
<td class="gt_row gt_center" headers="Model 1">no</td>
<td class="gt_row gt_center" headers="Model 2">no</td>
<td class="gt_row gt_center" headers="Model 3">no</td>
<td class="gt_row gt_center" headers="Model 4">yes</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Threeway interaction ESS wave X Education X Gender</td>
<td class="gt_row gt_center" headers="Model 1">no</td>
<td class="gt_row gt_center" headers="Model 2">no</td>
<td class="gt_row gt_center" headers="Model 3">no</td>
<td class="gt_row gt_center" headers="Model 4">yes</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Num.Obs.</td>
<td class="gt_row gt_center" headers="Model 1">112318</td>
<td class="gt_row gt_center" headers="Model 2">111279</td>
<td class="gt_row gt_center" headers="Model 3">111279</td>
<td class="gt_row gt_center" headers="Model 4">111279</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">R2 Marg.</td>
<td class="gt_row gt_center" headers="Model 1">0.276</td>
<td class="gt_row gt_center" headers="Model 2">0.322</td>
<td class="gt_row gt_center" headers="Model 3">0.322</td>
<td class="gt_row gt_center" headers="Model 4">0.326</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">R2 Cond.</td>
<td class="gt_row gt_center" headers="Model 1">0.442</td>
<td class="gt_row gt_center" headers="Model 2">0.482</td>
<td class="gt_row gt_center" headers="Model 3">0.483</td>
<td class="gt_row gt_center" headers="Model 4">0.485</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">AIC</td>
<td class="gt_row gt_center" headers="Model 1">52667.4</td>
<td class="gt_row gt_center" headers="Model 2">50065.7</td>
<td class="gt_row gt_center" headers="Model 3">49897.7</td>
<td class="gt_row gt_center" headers="Model 4">49925.9</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">BIC</td>
<td class="gt_row gt_center" headers="Model 1">52927.4</td>
<td class="gt_row gt_center" headers="Model 2">50354.3</td>
<td class="gt_row gt_center" headers="Model 3">50494.1</td>
<td class="gt_row gt_center" headers="Model 4">51003.3</td>
</tr>
</tbody><tfoot class="gt_sourcenotes">
<tr class="odd">
<td colspan="5" class="gt_sourcenote">+ p &lt; 0.1, * p &lt; 0.05, ** p &lt; 0.01, *** p &lt; 0.001</td>
</tr>
</tfoot>

</table>

</div>
<p>Voting for a radical right parties – regression coefficients</p>
</div>
</div>
</section>
</section>
<section id="alternative-models" class="level1" data-number="6">
<h1 data-number="6"><span class="header-section-number">6</span> Alternative models</h1>
<section id="logistic-regression-with-country-wave-fixed-effects-and-clustered-standard-errors." class="level2" data-number="6.1">
<h2 data-number="6.1" class="anchored" data-anchor-id="logistic-regression-with-country-wave-fixed-effects-and-clustered-standard-errors."><span class="header-section-number">6.1</span> Logistic regression with country-wave fixed effects and clustered standard errors.</h2>
<div id="tbl-table_b3" class="cell anchored">
<details class="code-fold">
<summary>Show the code</summary>
<div class="sourceCode cell-code" id="cb27"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a>models <span class="ot">&lt;-</span> <span class="fu">list</span>(</span>
<span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Multilevel Greens&quot;</span> <span class="ot">=</span> <span class="fu">glmer</span>(</span>
<span id="cb27-3"><a href="#cb27-3" aria-hidden="true" tabindex="-1"></a>    vote_green <span class="sc">~</span> cohort_fct <span class="sc">*</span> edu_fct <span class="sc">*</span> gndr_fct <span class="sc">+</span></span>
<span id="cb27-4"><a href="#cb27-4" aria-hidden="true" tabindex="-1"></a>      <span class="fu">factor</span>(age_group) <span class="sc">*</span> edu_fct <span class="sc">*</span> gndr_fct <span class="sc">+</span></span>
<span id="cb27-5"><a href="#cb27-5" aria-hidden="true" tabindex="-1"></a>      <span class="fu">factor</span>(essround) <span class="sc">*</span> edu_fct <span class="sc">*</span> gndr_fct <span class="sc">+</span></span>
<span id="cb27-6"><a href="#cb27-6" aria-hidden="true" tabindex="-1"></a>      immigrant <span class="sc">+</span></span>
<span id="cb27-7"><a href="#cb27-7" aria-hidden="true" tabindex="-1"></a>      <span class="fu">factor</span>(cntry_id) <span class="sc">+</span> (<span class="dv">1</span> <span class="sc">|</span> cntry_wave_id),</span>
<span id="cb27-8"><a href="#cb27-8" aria-hidden="true" tabindex="-1"></a>    <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">&quot;logit&quot;</span>),</span>
<span id="cb27-9"><a href="#cb27-9" aria-hidden="true" tabindex="-1"></a>    <span class="at">nAGQ =</span> <span class="dv">0</span>,</span>
<span id="cb27-10"><a href="#cb27-10" aria-hidden="true" tabindex="-1"></a>    <span class="at">data =</span> dfgreen</span>
<span id="cb27-11"><a href="#cb27-11" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb27-12"><a href="#cb27-12" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Logit Greens&quot;</span> <span class="ot">=</span> <span class="fu">glm</span>(</span>
<span id="cb27-13"><a href="#cb27-13" aria-hidden="true" tabindex="-1"></a>    vote_green <span class="sc">~</span> cohort_fct <span class="sc">*</span> edu_fct <span class="sc">*</span> gndr_fct <span class="sc">+</span></span>
<span id="cb27-14"><a href="#cb27-14" aria-hidden="true" tabindex="-1"></a>      <span class="fu">factor</span>(age_group) <span class="sc">*</span> edu_fct <span class="sc">*</span> gndr_fct <span class="sc">+</span></span>
<span id="cb27-15"><a href="#cb27-15" aria-hidden="true" tabindex="-1"></a>      <span class="fu">factor</span>(essround) <span class="sc">*</span> edu_fct <span class="sc">*</span> gndr_fct <span class="sc">+</span> immigrant <span class="sc">+</span></span>
<span id="cb27-16"><a href="#cb27-16" aria-hidden="true" tabindex="-1"></a>      <span class="fu">factor</span>(cntry_wave_id),</span>
<span id="cb27-17"><a href="#cb27-17" aria-hidden="true" tabindex="-1"></a>    <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">&quot;logit&quot;</span>), <span class="at">data =</span> dfgreen</span>
<span id="cb27-18"><a href="#cb27-18" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb27-19"><a href="#cb27-19" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Multilevel Radical Right&quot;</span> <span class="ot">=</span> <span class="fu">glmer</span>(</span>
<span id="cb27-20"><a href="#cb27-20" aria-hidden="true" tabindex="-1"></a>    vote_radright <span class="sc">~</span> cohort_fct <span class="sc">*</span> edu_fct <span class="sc">*</span> gndr_fct <span class="sc">+</span></span>
<span id="cb27-21"><a href="#cb27-21" aria-hidden="true" tabindex="-1"></a>      <span class="fu">factor</span>(age_group) <span class="sc">*</span> edu_fct <span class="sc">*</span> gndr_fct <span class="sc">+</span></span>
<span id="cb27-22"><a href="#cb27-22" aria-hidden="true" tabindex="-1"></a>      <span class="fu">factor</span>(essround) <span class="sc">*</span> edu_fct <span class="sc">*</span> gndr_fct <span class="sc">+</span></span>
<span id="cb27-23"><a href="#cb27-23" aria-hidden="true" tabindex="-1"></a>      immigrant <span class="sc">+</span> <span class="fu">factor</span>(cntry_id) <span class="sc">+</span> (<span class="dv">1</span> <span class="sc">|</span> cntry_wave_id),</span>
<span id="cb27-24"><a href="#cb27-24" aria-hidden="true" tabindex="-1"></a>    <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">&quot;logit&quot;</span>),</span>
<span id="cb27-25"><a href="#cb27-25" aria-hidden="true" tabindex="-1"></a>    <span class="at">nAGQ =</span> <span class="dv">0</span>,</span>
<span id="cb27-26"><a href="#cb27-26" aria-hidden="true" tabindex="-1"></a>    <span class="at">data =</span> dfradicalright</span>
<span id="cb27-27"><a href="#cb27-27" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb27-28"><a href="#cb27-28" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Logit Radical Right&quot;</span> <span class="ot">=</span> <span class="fu">glm</span>(</span>
<span id="cb27-29"><a href="#cb27-29" aria-hidden="true" tabindex="-1"></a>    vote_radright <span class="sc">~</span> cohort_fct <span class="sc">*</span> edu_fct <span class="sc">*</span> gndr_fct <span class="sc">+</span></span>
<span id="cb27-30"><a href="#cb27-30" aria-hidden="true" tabindex="-1"></a>      <span class="fu">factor</span>(age_group) <span class="sc">*</span> edu_fct <span class="sc">*</span> gndr_fct <span class="sc">+</span></span>
<span id="cb27-31"><a href="#cb27-31" aria-hidden="true" tabindex="-1"></a>      <span class="fu">factor</span>(essround) <span class="sc">*</span> edu_fct <span class="sc">*</span> gndr_fct <span class="sc">+</span> immigrant <span class="sc">+</span></span>
<span id="cb27-32"><a href="#cb27-32" aria-hidden="true" tabindex="-1"></a>      <span class="fu">factor</span>(cntry_wave_id),</span>
<span id="cb27-33"><a href="#cb27-33" aria-hidden="true" tabindex="-1"></a>    <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">&quot;logit&quot;</span>), <span class="at">data =</span> dfradicalright</span>
<span id="cb27-34"><a href="#cb27-34" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb27-35"><a href="#cb27-35" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb27-36"><a href="#cb27-36" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb27-37"><a href="#cb27-37" aria-hidden="true" tabindex="-1"></a>rows <span class="ot">&lt;-</span> <span class="fu">tribble</span>(</span>
<span id="cb27-38"><a href="#cb27-38" aria-hidden="true" tabindex="-1"></a>  <span class="sc">~</span>term, <span class="sc">~</span><span class="st">&quot;Model 1&quot;</span>, <span class="sc">~</span><span class="st">&quot;Model 2&quot;</span>, <span class="sc">~</span><span class="st">&quot;Model 3&quot;</span>, <span class="sc">~</span><span class="st">&quot;Model 4&quot;</span>,</span>
<span id="cb27-39"><a href="#cb27-39" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Interwar Generation&quot;</span>, <span class="st">&quot;ref.&quot;</span>, <span class="st">&quot;ref.&quot;</span>, <span class="st">&quot;ref.&quot;</span>, <span class="st">&quot;ref.&quot;</span>,</span>
<span id="cb27-40"><a href="#cb27-40" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Gender: male&quot;</span>, <span class="st">&quot;ref.&quot;</span>, <span class="st">&quot;ref.&quot;</span>, <span class="st">&quot;ref.&quot;</span>, <span class="st">&quot;ref.&quot;</span>,</span>
<span id="cb27-41"><a href="#cb27-41" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Age: 22-29&quot;</span>, <span class="st">&quot;ref.&quot;</span>, <span class="st">&quot;ref.&quot;</span>, <span class="st">&quot;ref.&quot;</span>, <span class="st">&quot;ref.&quot;</span>,</span>
<span id="cb27-42"><a href="#cb27-42" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Education: (Below) lower secondary&quot;</span>, <span class="st">&quot; &quot;</span>, <span class="st">&quot;ref.&quot;</span>, <span class="st">&quot;ref.&quot;</span>, <span class="st">&quot;ref.&quot;</span>,</span>
<span id="cb27-43"><a href="#cb27-43" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Generation X Education&quot;</span>, <span class="st">&quot;&quot;</span>, <span class="st">&quot;&quot;</span>, <span class="st">&quot;&quot;</span>, <span class="st">&quot;&quot;</span>,</span>
<span id="cb27-44"><a href="#cb27-44" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Generation X Female&quot;</span>, <span class="st">&quot;&quot;</span>, <span class="st">&quot;&quot;</span>, <span class="st">&quot;&quot;</span>, <span class="st">&quot;&quot;</span>,</span>
<span id="cb27-45"><a href="#cb27-45" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Education X Female&quot;</span>, <span class="st">&quot;&quot;</span>, <span class="st">&quot;&quot;</span>, <span class="st">&quot;&quot;</span>, <span class="st">&quot;&quot;</span>,</span>
<span id="cb27-46"><a href="#cb27-46" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Generation X Education X Female&quot;</span>, <span class="st">&quot;&quot;</span>, <span class="st">&quot;&quot;</span>, <span class="st">&quot;&quot;</span>, <span class="st">&quot;&quot;</span>,</span>
<span id="cb27-47"><a href="#cb27-47" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Country fixed effects&quot;</span>, <span class="st">&quot;yes&quot;</span>, <span class="st">&quot;&quot;</span>, <span class="st">&quot;yes&quot;</span>, <span class="st">&quot;&quot;</span>,</span>
<span id="cb27-48"><a href="#cb27-48" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Country-wave fixed effects&quot;</span>, <span class="st">&quot;&quot;</span>, <span class="st">&quot;yes&quot;</span>, <span class="st">&quot;&quot;</span>, <span class="st">&quot;yes&quot;</span>,</span>
<span id="cb27-49"><a href="#cb27-49" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Threeway interaction Age group X Education X Gender&quot;</span>, <span class="st">&quot;yes&quot;</span>, <span class="st">&quot;yes&quot;</span>, <span class="st">&quot;yes&quot;</span>, <span class="st">&quot;yes&quot;</span>,</span>
<span id="cb27-50"><a href="#cb27-50" aria-hidden="true" tabindex="-1"></a>  <span class="st">&quot;Threeway interaction ESS wave X Education X Gender&quot;</span>, <span class="st">&quot;yes&quot;</span>, <span class="st">&quot;yes&quot;</span>, <span class="st">&quot;yes&quot;</span>, <span class="st">&quot;yes&quot;</span></span>
<span id="cb27-51"><a href="#cb27-51" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb27-52"><a href="#cb27-52" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb27-53"><a href="#cb27-53" aria-hidden="true" tabindex="-1"></a><span class="fu">attr</span>(rows, <span class="st">&quot;position&quot;</span>) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">12</span>, <span class="dv">15</span>, <span class="dv">22</span>, <span class="dv">27</span>, <span class="dv">44</span>, <span class="dv">55</span>, <span class="dv">60</span>, <span class="dv">81</span>, <span class="dv">82</span>, <span class="dv">83</span>, <span class="dv">84</span>)</span>
<span id="cb27-54"><a href="#cb27-54" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb27-55"><a href="#cb27-55" aria-hidden="true" tabindex="-1"></a><span class="fu">modelsummary</span>(models,</span>
<span id="cb27-56"><a href="#cb27-56" aria-hidden="true" tabindex="-1"></a>  <span class="at">stars =</span> <span class="cn">TRUE</span>, <span class="at">fmt =</span> <span class="st">&quot;%.3f&quot;</span>,</span>
<span id="cb27-57"><a href="#cb27-57" aria-hidden="true" tabindex="-1"></a>  <span class="at">coef_map =</span> coefname,</span>
<span id="cb27-58"><a href="#cb27-58" aria-hidden="true" tabindex="-1"></a>  <span class="at">add_rows =</span> rows,</span>
<span id="cb27-59"><a href="#cb27-59" aria-hidden="true" tabindex="-1"></a>  <span class="at">gof_omit =</span> <span class="st">&quot;ICC|RMSE&quot;</span>,</span>
<span id="cb27-60"><a href="#cb27-60" aria-hidden="true" tabindex="-1"></a>  <span class="at">output =</span> <span class="fu">here</span>(<span class="st">&quot;tables&quot;</span>, <span class="st">&quot;appendix_table_e1.docx&quot;</span>)</span>
<span id="cb27-61"><a href="#cb27-61" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb27-62"><a href="#cb27-62" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb27-63"><a href="#cb27-63" aria-hidden="true" tabindex="-1"></a><span class="fu">modelsummary</span>(models,</span>
<span id="cb27-64"><a href="#cb27-64" aria-hidden="true" tabindex="-1"></a>  <span class="at">stars =</span> <span class="cn">TRUE</span>, <span class="at">fmt =</span> <span class="st">&quot;%.3f&quot;</span>,</span>
<span id="cb27-65"><a href="#cb27-65" aria-hidden="true" tabindex="-1"></a>  <span class="at">coef_map =</span> coefname,</span>
<span id="cb27-66"><a href="#cb27-66" aria-hidden="true" tabindex="-1"></a>  <span class="at">add_rows =</span> rows,</span>
<span id="cb27-67"><a href="#cb27-67" aria-hidden="true" tabindex="-1"></a>  <span class="at">gof_omit =</span> <span class="st">&quot;ICC|RMSE&quot;</span>,</span>
<span id="cb27-68"><a href="#cb27-68" aria-hidden="true" tabindex="-1"></a>  <span class="at">output =</span> <span class="st">&quot;gt&quot;</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
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<table class="gt_table caption-top table table-sm table-striped small" data-quarto-postprocess="true" data-quarto-disable-processing="false" data-quarto-bootstrap="false">
<thead>
<tr class="header gt_col_headings">
<th id="a-" class="gt_col_heading gt_columns_bottom_border gt_left" data-quarto-table-cell-role="th" scope="col"></th>
<th id="Multilevel-Greens" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col">Multilevel Greens</th>
<th id="Logit-Greens" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col">Logit Greens</th>
<th id="Multilevel-Radical-Right" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col">Multilevel Radical Right</th>
<th id="Logit-Radical-Right" class="gt_col_heading gt_columns_bottom_border gt_center" data-quarto-table-cell-role="th" scope="col">Logit Radical Right</th>
</tr>
</thead>
<tbody class="gt_table_body">
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Interwar Generation</td>
<td class="gt_row gt_center" headers="Multilevel Greens">ref.</td>
<td class="gt_row gt_center" headers="Logit Greens">ref.</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">ref.</td>
<td class="gt_row gt_center" headers="Logit Radical Right">ref.</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Post-War Generation</td>
<td class="gt_row gt_center" headers="Multilevel Greens">0.331</td>
<td class="gt_row gt_center" headers="Logit Greens">0.326</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">0.199</td>
<td class="gt_row gt_center" headers="Logit Radical Right">0.200</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(0.476)</td>
<td class="gt_row gt_center" headers="Logit Greens">(0.476)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.187)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.188)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">60-70s Generation</td>
<td class="gt_row gt_center" headers="Multilevel Greens">1.264*</td>
<td class="gt_row gt_center" headers="Logit Greens">1.259*</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">0.329</td>
<td class="gt_row gt_center" headers="Logit Radical Right">0.336</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(0.518)</td>
<td class="gt_row gt_center" headers="Logit Greens">(0.518)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.211)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.213)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">80s Generation</td>
<td class="gt_row gt_center" headers="Multilevel Greens">1.576**</td>
<td class="gt_row gt_center" headers="Logit Greens">1.571**</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">0.658**</td>
<td class="gt_row gt_center" headers="Logit Radical Right">0.668**</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(0.546)</td>
<td class="gt_row gt_center" headers="Logit Greens">(0.546)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.230)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.232)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">90s Generation</td>
<td class="gt_row gt_center" headers="Multilevel Greens">1.980***</td>
<td class="gt_row gt_center" headers="Logit Greens">1.977***</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">0.686**</td>
<td class="gt_row gt_center" headers="Logit Radical Right">0.695**</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(0.558)</td>
<td class="gt_row gt_center" headers="Logit Greens">(0.558)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.244)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.246)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Millennials</td>
<td class="gt_row gt_center" headers="Multilevel Greens">1.867**</td>
<td class="gt_row gt_center" headers="Logit Greens">1.861**</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">0.730*</td>
<td class="gt_row gt_center" headers="Logit Radical Right">0.742*</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(0.656)</td>
<td class="gt_row gt_center" headers="Logit Greens">(0.656)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.306)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.307)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Gender: male</td>
<td class="gt_row gt_center" headers="Multilevel Greens">ref.</td>
<td class="gt_row gt_center" headers="Logit Greens">ref.</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">ref.</td>
<td class="gt_row gt_center" headers="Logit Radical Right">ref.</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Gender: female</td>
<td class="gt_row gt_center" headers="Multilevel Greens">0.734</td>
<td class="gt_row gt_center" headers="Logit Greens">0.734</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">-0.985*</td>
<td class="gt_row gt_center" headers="Logit Radical Right">-0.983*</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(0.811)</td>
<td class="gt_row gt_center" headers="Logit Greens">(0.812)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.491)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.492)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Age: 22-29</td>
<td class="gt_row gt_center" headers="Multilevel Greens">ref.</td>
<td class="gt_row gt_center" headers="Logit Greens">ref.</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">ref.</td>
<td class="gt_row gt_center" headers="Logit Radical Right">ref.</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Age: 30-65</td>
<td class="gt_row gt_center" headers="Multilevel Greens">-0.451</td>
<td class="gt_row gt_center" headers="Logit Greens">-0.450</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">0.063</td>
<td class="gt_row gt_center" headers="Logit Radical Right">0.067</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(0.360)</td>
<td class="gt_row gt_center" headers="Logit Greens">(0.360)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.255)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.256)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Age: 66+</td>
<td class="gt_row gt_center" headers="Multilevel Greens">-0.263</td>
<td class="gt_row gt_center" headers="Logit Greens">-0.262</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">-0.151</td>
<td class="gt_row gt_center" headers="Logit Radical Right">-0.142</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(0.458)</td>
<td class="gt_row gt_center" headers="Logit Greens">(0.458)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.285)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.286)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Immigrant background</td>
<td class="gt_row gt_center" headers="Multilevel Greens">0.104***</td>
<td class="gt_row gt_center" headers="Logit Greens">0.104***</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">-0.226***</td>
<td class="gt_row gt_center" headers="Logit Radical Right">-0.226***</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(0.031)</td>
<td class="gt_row gt_center" headers="Logit Greens">(0.031)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.039)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.039)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Education: (Below) lower secondary</td>
<td class="gt_row gt_center" headers="Multilevel Greens"></td>
<td class="gt_row gt_center" headers="Logit Greens">ref.</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">ref.</td>
<td class="gt_row gt_center" headers="Logit Radical Right">ref.</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Education: upper secondary</td>
<td class="gt_row gt_center" headers="Multilevel Greens">0.441</td>
<td class="gt_row gt_center" headers="Logit Greens">0.451</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">-0.364</td>
<td class="gt_row gt_center" headers="Logit Radical Right">-0.368</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(0.817)</td>
<td class="gt_row gt_center" headers="Logit Greens">(0.817)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.430)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.432)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Education: tertiary</td>
<td class="gt_row gt_center" headers="Multilevel Greens">0.730</td>
<td class="gt_row gt_center" headers="Logit Greens">0.735</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">-0.198</td>
<td class="gt_row gt_center" headers="Logit Radical Right">-0.200</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(0.810)</td>
<td class="gt_row gt_center" headers="Logit Greens">(0.810)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.475)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.477)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Generation X Education</td>
<td class="gt_row gt_center" headers="Multilevel Greens"></td>
<td class="gt_row gt_center" headers="Logit Greens"></td>
<td class="gt_row gt_center" headers="Multilevel Radical Right"></td>
<td class="gt_row gt_center" headers="Logit Radical Right"></td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Post-War Generation # education: upper secondary</td>
<td class="gt_row gt_center" headers="Multilevel Greens">0.233</td>
<td class="gt_row gt_center" headers="Logit Greens">0.237</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">-0.035</td>
<td class="gt_row gt_center" headers="Logit Radical Right">-0.034</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(0.664)</td>
<td class="gt_row gt_center" headers="Logit Greens">(0.664)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.297)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.299)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Post-War Generation # education: tertiary</td>
<td class="gt_row gt_center" headers="Multilevel Greens">0.278</td>
<td class="gt_row gt_center" headers="Logit Greens">0.284</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">-0.704*</td>
<td class="gt_row gt_center" headers="Logit Radical Right">-0.705*</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(0.663)</td>
<td class="gt_row gt_center" headers="Logit Greens">(0.663)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.328)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.330)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">60-70s Generation # education: upper secondary</td>
<td class="gt_row gt_center" headers="Multilevel Greens">-0.372</td>
<td class="gt_row gt_center" headers="Logit Greens">-0.368</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">-0.026</td>
<td class="gt_row gt_center" headers="Logit Radical Right">-0.024</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(0.701)</td>
<td class="gt_row gt_center" headers="Logit Greens">(0.701)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.320)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.322)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">60-70s Generation # education: tertiary</td>
<td class="gt_row gt_center" headers="Multilevel Greens">-0.076</td>
<td class="gt_row gt_center" headers="Logit Greens">-0.069</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">-1.024**</td>
<td class="gt_row gt_center" headers="Logit Radical Right">-1.026**</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(0.696)</td>
<td class="gt_row gt_center" headers="Logit Greens">(0.696)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.354)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.356)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">80s Generation # education: upper secondary</td>
<td class="gt_row gt_center" headers="Multilevel Greens">-0.406</td>
<td class="gt_row gt_center" headers="Logit Greens">-0.401</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">-0.112</td>
<td class="gt_row gt_center" headers="Logit Radical Right">-0.110</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(0.727)</td>
<td class="gt_row gt_center" headers="Logit Greens">(0.727)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.338)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.340)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">80s Generation # education: tertiary</td>
<td class="gt_row gt_center" headers="Multilevel Greens">0.023</td>
<td class="gt_row gt_center" headers="Logit Greens">0.031</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">-1.302***</td>
<td class="gt_row gt_center" headers="Logit Radical Right">-1.303***</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(0.721)</td>
<td class="gt_row gt_center" headers="Logit Greens">(0.721)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.377)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.379)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Millennials # education: upper secondary</td>
<td class="gt_row gt_center" headers="Multilevel Greens">-0.310</td>
<td class="gt_row gt_center" headers="Logit Greens">-0.306</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">0.008</td>
<td class="gt_row gt_center" headers="Logit Radical Right">0.009</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(0.821)</td>
<td class="gt_row gt_center" headers="Logit Greens">(0.821)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.403)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.405)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Millennials # education: tertiary</td>
<td class="gt_row gt_center" headers="Multilevel Greens">-0.218</td>
<td class="gt_row gt_center" headers="Logit Greens">-0.209</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">-1.394**</td>
<td class="gt_row gt_center" headers="Logit Radical Right">-1.397**</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(0.811)</td>
<td class="gt_row gt_center" headers="Logit Greens">(0.811)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.443)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.444)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Generation X Female</td>
<td class="gt_row gt_center" headers="Multilevel Greens"></td>
<td class="gt_row gt_center" headers="Logit Greens"></td>
<td class="gt_row gt_center" headers="Multilevel Radical Right"></td>
<td class="gt_row gt_center" headers="Logit Radical Right"></td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Post-War Generation # Female</td>
<td class="gt_row gt_center" headers="Multilevel Greens">0.011</td>
<td class="gt_row gt_center" headers="Logit Greens">0.013</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">0.575*</td>
<td class="gt_row gt_center" headers="Logit Radical Right">0.573*</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(0.552)</td>
<td class="gt_row gt_center" headers="Logit Greens">(0.552)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.274)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.276)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">60-70s Generation # Female</td>
<td class="gt_row gt_center" headers="Multilevel Greens">-0.469</td>
<td class="gt_row gt_center" headers="Logit Greens">-0.469</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">0.774*</td>
<td class="gt_row gt_center" headers="Logit Radical Right">0.771*</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(0.607)</td>
<td class="gt_row gt_center" headers="Logit Greens">(0.607)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.307)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.309)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">80s Generation # Female</td>
<td class="gt_row gt_center" headers="Multilevel Greens">-0.488</td>
<td class="gt_row gt_center" headers="Logit Greens">-0.488</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">0.622+</td>
<td class="gt_row gt_center" headers="Logit Radical Right">0.618+</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(0.642)</td>
<td class="gt_row gt_center" headers="Logit Greens">(0.642)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.335)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.337)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">90s Generation # Female</td>
<td class="gt_row gt_center" headers="Multilevel Greens">-0.976</td>
<td class="gt_row gt_center" headers="Logit Greens">-0.977</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">1.126**</td>
<td class="gt_row gt_center" headers="Logit Radical Right">1.125**</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(0.664)</td>
<td class="gt_row gt_center" headers="Logit Greens">(0.664)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.352)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.354)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Millennials # Female</td>
<td class="gt_row gt_center" headers="Multilevel Greens">-0.596</td>
<td class="gt_row gt_center" headers="Logit Greens">-0.596</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">1.207**</td>
<td class="gt_row gt_center" headers="Logit Radical Right">1.200**</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(0.795)</td>
<td class="gt_row gt_center" headers="Logit Greens">(0.795)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.443)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.445)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Education X Female</td>
<td class="gt_row gt_center" headers="Multilevel Greens"></td>
<td class="gt_row gt_center" headers="Logit Greens"></td>
<td class="gt_row gt_center" headers="Multilevel Radical Right"></td>
<td class="gt_row gt_center" headers="Logit Radical Right"></td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Education: upper secondary # Female</td>
<td class="gt_row gt_center" headers="Multilevel Greens">-0.961</td>
<td class="gt_row gt_center" headers="Logit Greens">-0.964</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">0.294</td>
<td class="gt_row gt_center" headers="Logit Radical Right">0.291</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(1.087)</td>
<td class="gt_row gt_center" headers="Logit Greens">(1.087)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.640)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.642)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Education: tertiary # female</td>
<td class="gt_row gt_center" headers="Multilevel Greens">-0.144</td>
<td class="gt_row gt_center" headers="Logit Greens">-0.140</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">0.409</td>
<td class="gt_row gt_center" headers="Logit Radical Right">0.410</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(1.056)</td>
<td class="gt_row gt_center" headers="Logit Greens">(1.056)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.807)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.811)</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Generation X Education X Female</td>
<td class="gt_row gt_center" headers="Multilevel Greens"></td>
<td class="gt_row gt_center" headers="Logit Greens"></td>
<td class="gt_row gt_center" headers="Multilevel Radical Right"></td>
<td class="gt_row gt_center" headers="Logit Radical Right"></td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Post-War Generation # Educ.: upper secondary # Female</td>
<td class="gt_row gt_center" headers="Multilevel Greens">0.933</td>
<td class="gt_row gt_center" headers="Logit Greens">0.932</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">-0.486</td>
<td class="gt_row gt_center" headers="Logit Radical Right">-0.485</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(0.883)</td>
<td class="gt_row gt_center" headers="Logit Greens">(0.883)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.448)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.451)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">60-70s Generation # Educ.: upper secondary # Female</td>
<td class="gt_row gt_center" headers="Multilevel Greens">1.277</td>
<td class="gt_row gt_center" headers="Logit Greens">1.279</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">-0.608</td>
<td class="gt_row gt_center" headers="Logit Radical Right">-0.606</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(0.926)</td>
<td class="gt_row gt_center" headers="Logit Greens">(0.926)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.480)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.484)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">80s Generation # Educ.: upper secondary # Female</td>
<td class="gt_row gt_center" headers="Multilevel Greens">1.329</td>
<td class="gt_row gt_center" headers="Logit Greens">1.330</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">-0.372</td>
<td class="gt_row gt_center" headers="Logit Radical Right">-0.369</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(0.955)</td>
<td class="gt_row gt_center" headers="Logit Greens">(0.956)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.508)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.511)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">90s Generation # Educ.: upper secondary # Female</td>
<td class="gt_row gt_center" headers="Multilevel Greens">1.598+</td>
<td class="gt_row gt_center" headers="Logit Greens">1.601+</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">-0.901+</td>
<td class="gt_row gt_center" headers="Logit Radical Right">-0.902+</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(0.972)</td>
<td class="gt_row gt_center" headers="Logit Greens">(0.972)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.521)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.525)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Millennials # Educ.: upper secondary # Female</td>
<td class="gt_row gt_center" headers="Multilevel Greens">1.216</td>
<td class="gt_row gt_center" headers="Logit Greens">1.218</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">-0.752</td>
<td class="gt_row gt_center" headers="Logit Radical Right">-0.746</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(1.075)</td>
<td class="gt_row gt_center" headers="Logit Greens">(1.075)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.602)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.605)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Post-War Generation # Educ.: tertiary # Female</td>
<td class="gt_row gt_center" headers="Multilevel Greens">0.098</td>
<td class="gt_row gt_center" headers="Logit Greens">0.092</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">-0.374</td>
<td class="gt_row gt_center" headers="Logit Radical Right">-0.376</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(0.857)</td>
<td class="gt_row gt_center" headers="Logit Greens">(0.857)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.620)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.625)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">60-70s Generation # Educ.: tertiary # Female</td>
<td class="gt_row gt_center" headers="Multilevel Greens">0.440</td>
<td class="gt_row gt_center" headers="Logit Greens">0.436</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">-0.785</td>
<td class="gt_row gt_center" headers="Logit Radical Right">-0.785</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(0.896)</td>
<td class="gt_row gt_center" headers="Logit Greens">(0.896)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.656)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.661)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">80s Generation # Educ.: tertiary # Female</td>
<td class="gt_row gt_center" headers="Multilevel Greens">0.334</td>
<td class="gt_row gt_center" headers="Logit Greens">0.329</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">-0.716</td>
<td class="gt_row gt_center" headers="Logit Radical Right">-0.716</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(0.925)</td>
<td class="gt_row gt_center" headers="Logit Greens">(0.925)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.688)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.693)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">90s Generation # Educ.: tertiary # Female</td>
<td class="gt_row gt_center" headers="Multilevel Greens">0.911</td>
<td class="gt_row gt_center" headers="Logit Greens">0.908</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">-1.104</td>
<td class="gt_row gt_center" headers="Logit Radical Right">-1.106</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" "></td>
<td class="gt_row gt_center" headers="Multilevel Greens">(0.941)</td>
<td class="gt_row gt_center" headers="Logit Greens">(0.941)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">(0.699)</td>
<td class="gt_row gt_center" headers="Logit Radical Right">(0.704)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Millennials # Educ.: tertiary # Female</td>
<td class="gt_row gt_center" headers="Multilevel Greens">0.602</td>
<td class="gt_row gt_center" headers="Logit Greens">0.597</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">-1.350+</td>
<td class="gt_row gt_center" headers="Logit Radical Right">-1.347+</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" " style="border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #000000"></td>
<td class="gt_row gt_center" headers="Multilevel Greens" style="border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #000000">(1.042)</td>
<td class="gt_row gt_center" headers="Logit Greens" style="border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #000000">(1.042)</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right" style="border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #000000">(0.773)</td>
<td class="gt_row gt_center" headers="Logit Radical Right" style="border-bottom-width: 1px; border-bottom-style: solid; border-bottom-color: #000000">(0.776)</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Country fixed effects</td>
<td class="gt_row gt_center" headers="Multilevel Greens">yes</td>
<td class="gt_row gt_center" headers="Logit Greens"></td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">yes</td>
<td class="gt_row gt_center" headers="Logit Radical Right"></td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Country-wave fixed effects</td>
<td class="gt_row gt_center" headers="Multilevel Greens"></td>
<td class="gt_row gt_center" headers="Logit Greens">yes</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right"></td>
<td class="gt_row gt_center" headers="Logit Radical Right">yes</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Threeway interaction Age group X Education X Gender</td>
<td class="gt_row gt_center" headers="Multilevel Greens">yes</td>
<td class="gt_row gt_center" headers="Logit Greens">yes</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">yes</td>
<td class="gt_row gt_center" headers="Logit Radical Right">yes</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Threeway interaction ESS wave X Education X Gender</td>
<td class="gt_row gt_center" headers="Multilevel Greens">yes</td>
<td class="gt_row gt_center" headers="Logit Greens">yes</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">yes</td>
<td class="gt_row gt_center" headers="Logit Radical Right">yes</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">Num.Obs.</td>
<td class="gt_row gt_center" headers="Multilevel Greens">124209</td>
<td class="gt_row gt_center" headers="Logit Greens">124209</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">111279</td>
<td class="gt_row gt_center" headers="Logit Radical Right">111279</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">R2 Marg.</td>
<td class="gt_row gt_center" headers="Multilevel Greens">0.330</td>
<td class="gt_row gt_center" headers="Logit Greens"></td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">0.326</td>
<td class="gt_row gt_center" headers="Logit Radical Right"></td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">R2 Cond.</td>
<td class="gt_row gt_center" headers="Multilevel Greens">0.362</td>
<td class="gt_row gt_center" headers="Logit Greens"></td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">0.485</td>
<td class="gt_row gt_center" headers="Logit Radical Right"></td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">AIC</td>
<td class="gt_row gt_center" headers="Multilevel Greens">59383.7</td>
<td class="gt_row gt_center" headers="Logit Greens">59157.1</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">49925.9</td>
<td class="gt_row gt_center" headers="Logit Radical Right">49615.4</td>
</tr>
<tr class="odd">
<td class="gt_row gt_left" headers=" ">BIC</td>
<td class="gt_row gt_center" headers="Multilevel Greens">60483.2</td>
<td class="gt_row gt_center" headers="Logit Greens">61034.9</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right">51003.3</td>
<td class="gt_row gt_center" headers="Logit Radical Right">51404.7</td>
</tr>
<tr class="even">
<td class="gt_row gt_left" headers=" ">Log.Lik.</td>
<td class="gt_row gt_center" headers="Multilevel Greens"></td>
<td class="gt_row gt_center" headers="Logit Greens">-29385.529</td>
<td class="gt_row gt_center" headers="Multilevel Radical Right"></td>
<td class="gt_row gt_center" headers="Logit Radical Right">-24621.710</td>
</tr>
</tbody><tfoot class="gt_sourcenotes">
<tr class="odd">
<td colspan="5" class="gt_sourcenote">+ p &lt; 0.1, * p &lt; 0.05, ** p &lt; 0.01, *** p &lt; 0.001</td>
</tr>
</tfoot>

</table>

</div>
<p>Voting for a radical right parties and green parties–multilevel and logitistic regression models in comparison</p>
</div>
</div>
</section>
</section>
<section id="fine-grained-cohorts" class="level1" data-number="7">
<h1 data-number="7"><span class="header-section-number">7</span> Fine-grained cohorts</h1>
<section id="multilevel-model-with-three-way-interaction-voting-for-the-greens-alternative-cohorts" class="level2" data-number="7.1">
<h2 data-number="7.1" class="anchored" data-anchor-id="multilevel-model-with-three-way-interaction-voting-for-the-greens-alternative-cohorts"><span class="header-section-number">7.1</span> Multilevel model with three-way interaction: Voting for the greens (alternative cohorts)</h2>
<div class="cell">
<details class="code-fold">
<summary>Show the code</summary>
<div class="sourceCode cell-code" id="cb28"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1" aria-hidden="true" tabindex="-1"></a><span class="co"># model</span></span>
<span id="cb28-2"><a href="#cb28-2" aria-hidden="true" tabindex="-1"></a>three_green_alt <span class="ot">&lt;-</span> <span class="fu">glmer</span>(</span>
<span id="cb28-3"><a href="#cb28-3" aria-hidden="true" tabindex="-1"></a>  vote_green <span class="sc">~</span></span>
<span id="cb28-4"><a href="#cb28-4" aria-hidden="true" tabindex="-1"></a>    cohort_alt_fct <span class="sc">*</span> edu_fct <span class="sc">*</span> gndr_fct <span class="sc">+</span></span>
<span id="cb28-5"><a href="#cb28-5" aria-hidden="true" tabindex="-1"></a>    <span class="fu">factor</span>(age_group) <span class="sc">*</span> edu_fct <span class="sc">*</span> gndr_fct <span class="sc">+</span></span>
<span id="cb28-6"><a href="#cb28-6" aria-hidden="true" tabindex="-1"></a>    <span class="fu">factor</span>(essround) <span class="sc">*</span> edu_fct <span class="sc">*</span> gndr_fct <span class="sc">+</span></span>
<span id="cb28-7"><a href="#cb28-7" aria-hidden="true" tabindex="-1"></a>    immigrant <span class="sc">+</span> <span class="fu">factor</span>(cntry_id) <span class="sc">+</span></span>
<span id="cb28-8"><a href="#cb28-8" aria-hidden="true" tabindex="-1"></a>    (<span class="dv">1</span> <span class="sc">|</span> cntry_wave_id),</span>
<span id="cb28-9"><a href="#cb28-9" aria-hidden="true" tabindex="-1"></a>  <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">&quot;logit&quot;</span>),</span>
<span id="cb28-10"><a href="#cb28-10" aria-hidden="true" tabindex="-1"></a>  <span class="at">nAGQ =</span> <span class="dv">0</span>,</span>
<span id="cb28-11"><a href="#cb28-11" aria-hidden="true" tabindex="-1"></a>  <span class="at">data =</span> dfgreen</span>
<span id="cb28-12"><a href="#cb28-12" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb28-13"><a href="#cb28-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-14"><a href="#cb28-14" aria-hidden="true" tabindex="-1"></a><span class="co"># predicted probabilities</span></span>
<span id="cb28-15"><a href="#cb28-15" aria-hidden="true" tabindex="-1"></a>pp_three_green_alt <span class="ot">&lt;-</span> <span class="fu">predictions</span>(three_green_alt,</span>
<span id="cb28-16"><a href="#cb28-16" aria-hidden="true" tabindex="-1"></a>  <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&quot;gndr_fct&quot;</span>, <span class="st">&quot;cohort_alt_fct&quot;</span>, <span class="st">&quot;edu_fct&quot;</span>),</span>
<span id="cb28-17"><a href="#cb28-17" aria-hidden="true" tabindex="-1"></a>  <span class="at">newdata =</span> <span class="fu">datagrid</span>(</span>
<span id="cb28-18"><a href="#cb28-18" aria-hidden="true" tabindex="-1"></a>    <span class="at">gndr_fct =</span> <span class="fu">c</span>(<span class="st">&quot;men&quot;</span>, <span class="st">&quot;women&quot;</span>),</span>
<span id="cb28-19"><a href="#cb28-19" aria-hidden="true" tabindex="-1"></a>    <span class="at">cohort_alt_fct =</span> <span class="fu">c</span>(</span>
<span id="cb28-20"><a href="#cb28-20" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;-1929&quot;</span>,</span>
<span id="cb28-21"><a href="#cb28-21" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;1930s&quot;</span>,</span>
<span id="cb28-22"><a href="#cb28-22" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;1940s&quot;</span>,</span>
<span id="cb28-23"><a href="#cb28-23" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;1950s&quot;</span>,</span>
<span id="cb28-24"><a href="#cb28-24" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;1960s&quot;</span>,</span>
<span id="cb28-25"><a href="#cb28-25" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;1970s&quot;</span>,</span>
<span id="cb28-26"><a href="#cb28-26" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;1980s&quot;</span>,</span>
<span id="cb28-27"><a href="#cb28-27" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;1990+&quot;</span></span>
<span id="cb28-28"><a href="#cb28-28" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb28-29"><a href="#cb28-29" aria-hidden="true" tabindex="-1"></a>    <span class="at">edu_fct =</span> <span class="fu">c</span>(</span>
<span id="cb28-30"><a href="#cb28-30" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;(below) lower secondary&quot;</span>,</span>
<span id="cb28-31"><a href="#cb28-31" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;upper secondary&quot;</span>,</span>
<span id="cb28-32"><a href="#cb28-32" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;tertiary&quot;</span></span>
<span id="cb28-33"><a href="#cb28-33" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb28-34"><a href="#cb28-34" aria-hidden="true" tabindex="-1"></a>    <span class="at">grid_type =</span> <span class="st">&quot;counterfactual&quot;</span></span>
<span id="cb28-35"><a href="#cb28-35" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb28-36"><a href="#cb28-36" aria-hidden="true" tabindex="-1"></a>    <span class="at">re.form =</span> <span class="cn">NA</span></span>
<span id="cb28-37"><a href="#cb28-37" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb28-38"><a href="#cb28-38" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-39"><a href="#cb28-39" aria-hidden="true" tabindex="-1"></a><span class="co"># plot</span></span>
<span id="cb28-40"><a href="#cb28-40" aria-hidden="true" tabindex="-1"></a>plot_pp_three_green_alt <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(</span>
<span id="cb28-41"><a href="#cb28-41" aria-hidden="true" tabindex="-1"></a>  <span class="at">data =</span> pp_three_green_alt,</span>
<span id="cb28-42"><a href="#cb28-42" aria-hidden="true" tabindex="-1"></a>  <span class="fu">aes</span>(</span>
<span id="cb28-43"><a href="#cb28-43" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> cohort_alt_fct, <span class="at">y =</span> estimate, <span class="at">ymin =</span> conf.low,</span>
<span id="cb28-44"><a href="#cb28-44" aria-hidden="true" tabindex="-1"></a>    <span class="at">ymax =</span> conf.high, <span class="at">color =</span> edu_fct</span>
<span id="cb28-45"><a href="#cb28-45" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb28-46"><a href="#cb28-46" aria-hidden="true" tabindex="-1"></a>) <span class="sc">+</span></span>
<span id="cb28-47"><a href="#cb28-47" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept =</span> <span class="dv">0</span>, <span class="at">color =</span> <span class="st">&quot;grey70&quot;</span>) <span class="sc">+</span></span>
<span id="cb28-48"><a href="#cb28-48" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_pointrange</span>(<span class="at">position =</span> <span class="fu">position_dodge</span>(<span class="at">width =</span> <span class="fu">c</span>(<span class="fl">0.2</span>))) <span class="sc">+</span></span>
<span id="cb28-49"><a href="#cb28-49" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb28-50"><a href="#cb28-50" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> <span class="cn">NULL</span>, <span class="at">y =</span> <span class="st">&quot;Predicted probability&quot;</span>,</span>
<span id="cb28-51"><a href="#cb28-51" aria-hidden="true" tabindex="-1"></a>    <span class="at">color =</span> <span class="cn">NULL</span></span>
<span id="cb28-52"><a href="#cb28-52" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb28-53"><a href="#cb28-53" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_viridis_d</span>(<span class="at">option =</span> <span class="st">&quot;plasma&quot;</span>, <span class="at">end =</span> .<span class="dv">8</span>) <span class="sc">+</span></span>
<span id="cb28-54"><a href="#cb28-54" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_light</span>() <span class="sc">+</span></span>
<span id="cb28-55"><a href="#cb28-55" aria-hidden="true" tabindex="-1"></a>  <span class="fu">my_theme</span>() <span class="sc">+</span></span>
<span id="cb28-56"><a href="#cb28-56" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;top&quot;</span>) <span class="sc">+</span></span>
<span id="cb28-57"><a href="#cb28-57" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>gndr_fct)</span>
<span id="cb28-58"><a href="#cb28-58" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-59"><a href="#cb28-59" aria-hidden="true" tabindex="-1"></a><span class="co"># marginal effects</span></span>
<span id="cb28-60"><a href="#cb28-60" aria-hidden="true" tabindex="-1"></a>me_three_green_alt <span class="ot">&lt;-</span> <span class="fu">avg_slopes</span>(</span>
<span id="cb28-61"><a href="#cb28-61" aria-hidden="true" tabindex="-1"></a>  three_green_alt,</span>
<span id="cb28-62"><a href="#cb28-62" aria-hidden="true" tabindex="-1"></a>  <span class="at">variables =</span> <span class="st">&quot;edu_fct&quot;</span>,</span>
<span id="cb28-63"><a href="#cb28-63" aria-hidden="true" tabindex="-1"></a>  <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&quot;cohort_alt_fct&quot;</span>, <span class="st">&quot;gndr_fct&quot;</span>),</span>
<span id="cb28-64"><a href="#cb28-64" aria-hidden="true" tabindex="-1"></a>  <span class="at">newdata =</span> <span class="fu">datagrid</span>(</span>
<span id="cb28-65"><a href="#cb28-65" aria-hidden="true" tabindex="-1"></a>    <span class="at">gndr_fct =</span> <span class="fu">c</span>(<span class="st">&quot;men&quot;</span>, <span class="st">&quot;women&quot;</span>),</span>
<span id="cb28-66"><a href="#cb28-66" aria-hidden="true" tabindex="-1"></a>    <span class="at">cohort_alt_fct =</span> <span class="fu">c</span>(</span>
<span id="cb28-67"><a href="#cb28-67" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;-1929&quot;</span>,</span>
<span id="cb28-68"><a href="#cb28-68" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;1930s&quot;</span>,</span>
<span id="cb28-69"><a href="#cb28-69" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;1940s&quot;</span>,</span>
<span id="cb28-70"><a href="#cb28-70" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;1950s&quot;</span>,</span>
<span id="cb28-71"><a href="#cb28-71" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;1960s&quot;</span>,</span>
<span id="cb28-72"><a href="#cb28-72" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;1970s&quot;</span>,</span>
<span id="cb28-73"><a href="#cb28-73" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;1980s&quot;</span>,</span>
<span id="cb28-74"><a href="#cb28-74" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;1990+&quot;</span></span>
<span id="cb28-75"><a href="#cb28-75" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb28-76"><a href="#cb28-76" aria-hidden="true" tabindex="-1"></a>    <span class="at">edu_fct =</span> <span class="fu">c</span>(</span>
<span id="cb28-77"><a href="#cb28-77" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;(below) lower secondary&quot;</span>,</span>
<span id="cb28-78"><a href="#cb28-78" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;upper secondary&quot;</span>,</span>
<span id="cb28-79"><a href="#cb28-79" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;tertiary&quot;</span></span>
<span id="cb28-80"><a href="#cb28-80" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb28-81"><a href="#cb28-81" aria-hidden="true" tabindex="-1"></a>    <span class="at">grid_type =</span> <span class="st">&quot;counterfactual&quot;</span></span>
<span id="cb28-82"><a href="#cb28-82" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb28-83"><a href="#cb28-83" aria-hidden="true" tabindex="-1"></a>    <span class="at">re.form =</span> <span class="cn">NA</span></span>
<span id="cb28-84"><a href="#cb28-84" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb28-85"><a href="#cb28-85" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-86"><a href="#cb28-86" aria-hidden="true" tabindex="-1"></a>me_three_green_alt <span class="ot">&lt;-</span> me_three_green_alt <span class="sc">%&gt;%</span></span>
<span id="cb28-87"><a href="#cb28-87" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">contrast =</span> <span class="fu">case_when</span>(</span>
<span id="cb28-88"><a href="#cb28-88" aria-hidden="true" tabindex="-1"></a>    contrast <span class="sc">==</span> <span class="st">&quot;tertiary - (below) lower secondary&quot;</span> <span class="sc">~</span></span>
<span id="cb28-89"><a href="#cb28-89" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;high vs low edu&quot;</span>,</span>
<span id="cb28-90"><a href="#cb28-90" aria-hidden="true" tabindex="-1"></a>    contrast <span class="sc">==</span> <span class="st">&quot;upper secondary - (below) lower secondary&quot;</span> <span class="sc">~</span></span>
<span id="cb28-91"><a href="#cb28-91" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;medium vs low edu&quot;</span></span>
<span id="cb28-92"><a href="#cb28-92" aria-hidden="true" tabindex="-1"></a>  ))</span>
<span id="cb28-93"><a href="#cb28-93" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-94"><a href="#cb28-94" aria-hidden="true" tabindex="-1"></a>plot_me_three_green_alt <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(</span>
<span id="cb28-95"><a href="#cb28-95" aria-hidden="true" tabindex="-1"></a>  <span class="at">data =</span> me_three_green_alt,</span>
<span id="cb28-96"><a href="#cb28-96" aria-hidden="true" tabindex="-1"></a>  <span class="fu">aes</span>(</span>
<span id="cb28-97"><a href="#cb28-97" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> cohort_alt_fct,</span>
<span id="cb28-98"><a href="#cb28-98" aria-hidden="true" tabindex="-1"></a>    <span class="at">y =</span> estimate,</span>
<span id="cb28-99"><a href="#cb28-99" aria-hidden="true" tabindex="-1"></a>    <span class="at">ymin =</span> conf.low,</span>
<span id="cb28-100"><a href="#cb28-100" aria-hidden="true" tabindex="-1"></a>    <span class="at">ymax =</span> conf.high,</span>
<span id="cb28-101"><a href="#cb28-101" aria-hidden="true" tabindex="-1"></a>    <span class="at">color =</span> contrast</span>
<span id="cb28-102"><a href="#cb28-102" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb28-103"><a href="#cb28-103" aria-hidden="true" tabindex="-1"></a>) <span class="sc">+</span></span>
<span id="cb28-104"><a href="#cb28-104" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept =</span> <span class="dv">0</span>, <span class="at">color =</span> <span class="st">&quot;grey70&quot;</span>) <span class="sc">+</span></span>
<span id="cb28-105"><a href="#cb28-105" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_pointrange</span>(<span class="at">position =</span> <span class="fu">position_dodge</span>(<span class="at">width =</span> <span class="fu">c</span>(<span class="fl">0.2</span>))) <span class="sc">+</span></span>
<span id="cb28-106"><a href="#cb28-106" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb28-107"><a href="#cb28-107" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> <span class="cn">NULL</span>,</span>
<span id="cb28-108"><a href="#cb28-108" aria-hidden="true" tabindex="-1"></a>    <span class="at">color =</span> <span class="st">&quot;Education&quot;</span>,</span>
<span id="cb28-109"><a href="#cb28-109" aria-hidden="true" tabindex="-1"></a>    <span class="at">y =</span> <span class="st">&quot;Marginal effect of education&quot;</span></span>
<span id="cb28-110"><a href="#cb28-110" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb28-111"><a href="#cb28-111" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;#fe9f6d&quot;</span>, <span class="st">&quot;#8c2981&quot;</span>)) <span class="sc">+</span></span>
<span id="cb28-112"><a href="#cb28-112" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>gndr_fct) <span class="sc">+</span></span>
<span id="cb28-113"><a href="#cb28-113" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_light</span>() <span class="sc">+</span></span>
<span id="cb28-114"><a href="#cb28-114" aria-hidden="true" tabindex="-1"></a>  <span class="fu">my_theme</span>() <span class="sc">+</span></span>
<span id="cb28-115"><a href="#cb28-115" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;top&quot;</span>)</span>
<span id="cb28-116"><a href="#cb28-116" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-117"><a href="#cb28-117" aria-hidden="true" tabindex="-1"></a><span class="co"># Combine the plots vertically</span></span>
<span id="cb28-118"><a href="#cb28-118" aria-hidden="true" tabindex="-1"></a>combined_plot <span class="ot">&lt;-</span> plot_pp_three_green_alt <span class="sc">/</span> plot_me_three_green_alt</span>
<span id="cb28-119"><a href="#cb28-119" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-120"><a href="#cb28-120" aria-hidden="true" tabindex="-1"></a><span class="co"># Save the combined plot</span></span>
<span id="cb28-121"><a href="#cb28-121" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(</span>
<span id="cb28-122"><a href="#cb28-122" aria-hidden="true" tabindex="-1"></a>  <span class="at">filename =</span> <span class="fu">here</span>(<span class="st">&quot;figs&quot;</span>, <span class="st">&quot;final&quot;</span>, <span class="st">&quot;appendix_fig_f1.png&quot;</span>),</span>
<span id="cb28-123"><a href="#cb28-123" aria-hidden="true" tabindex="-1"></a>  <span class="at">plot =</span> combined_plot,</span>
<span id="cb28-124"><a href="#cb28-124" aria-hidden="true" tabindex="-1"></a>  <span class="at">units =</span> <span class="fu">c</span>(<span class="st">&quot;in&quot;</span>),</span>
<span id="cb28-125"><a href="#cb28-125" aria-hidden="true" tabindex="-1"></a>  <span class="at">width =</span> <span class="dv">8</span>,</span>
<span id="cb28-126"><a href="#cb28-126" aria-hidden="true" tabindex="-1"></a>  <span class="at">height =</span> <span class="dv">10</span>, <span class="co"># Adjust height to fit two plots</span></span>
<span id="cb28-127"><a href="#cb28-127" aria-hidden="true" tabindex="-1"></a>  <span class="at">dpi =</span> <span class="dv">300</span></span>
<span id="cb28-128"><a href="#cb28-128" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb28-129"><a href="#cb28-129" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb28-130"><a href="#cb28-130" aria-hidden="true" tabindex="-1"></a>combined_plot</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
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<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-fig_f1-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<img role="img" 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" class="img-fluid figure-img" width="768">
</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-fig_f1-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure 19: Voting for a green party by cohort and education (three-way interaction)
</figcaption>
</figure>
</div>
</div>
</div>
</section>
<section id="multilevel-model-with-three-way-interaction-voting-for-the-radical-right-alternative-cohorts" class="level2" data-number="7.2">
<h2 data-number="7.2" class="anchored" data-anchor-id="multilevel-model-with-three-way-interaction-voting-for-the-radical-right-alternative-cohorts"><span class="header-section-number">7.2</span> Multilevel model with three-way interaction: Voting for the radical right (alternative cohorts)</h2>
<div class="cell">
<details class="code-fold">
<summary>Show the code</summary>
<div class="sourceCode cell-code" id="cb29"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a><span class="co"># model</span></span>
<span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a>three_rr_alt <span class="ot">&lt;-</span> <span class="fu">glmer</span>(</span>
<span id="cb29-3"><a href="#cb29-3" aria-hidden="true" tabindex="-1"></a>  vote_radright <span class="sc">~</span></span>
<span id="cb29-4"><a href="#cb29-4" aria-hidden="true" tabindex="-1"></a>    cohort_alt_fct <span class="sc">*</span> edu_fct <span class="sc">*</span> gndr_fct <span class="sc">+</span></span>
<span id="cb29-5"><a href="#cb29-5" aria-hidden="true" tabindex="-1"></a>    <span class="fu">factor</span>(age_group) <span class="sc">*</span> edu_fct <span class="sc">*</span> gndr_fct <span class="sc">+</span></span>
<span id="cb29-6"><a href="#cb29-6" aria-hidden="true" tabindex="-1"></a>    <span class="fu">factor</span>(essround) <span class="sc">*</span> edu_fct <span class="sc">*</span> gndr_fct <span class="sc">+</span></span>
<span id="cb29-7"><a href="#cb29-7" aria-hidden="true" tabindex="-1"></a>    immigrant <span class="sc">+</span> <span class="fu">factor</span>(cntry_id) <span class="sc">+</span></span>
<span id="cb29-8"><a href="#cb29-8" aria-hidden="true" tabindex="-1"></a>    (<span class="dv">1</span> <span class="sc">|</span> cntry_wave_id),</span>
<span id="cb29-9"><a href="#cb29-9" aria-hidden="true" tabindex="-1"></a>  <span class="at">family =</span> <span class="fu">binomial</span>(<span class="at">link =</span> <span class="st">&quot;logit&quot;</span>),</span>
<span id="cb29-10"><a href="#cb29-10" aria-hidden="true" tabindex="-1"></a>  <span class="at">nAGQ =</span> <span class="dv">0</span>,</span>
<span id="cb29-11"><a href="#cb29-11" aria-hidden="true" tabindex="-1"></a>  <span class="at">data =</span> dfradicalright</span>
<span id="cb29-12"><a href="#cb29-12" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb29-13"><a href="#cb29-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb29-14"><a href="#cb29-14" aria-hidden="true" tabindex="-1"></a><span class="co"># predicted probabilities</span></span>
<span id="cb29-15"><a href="#cb29-15" aria-hidden="true" tabindex="-1"></a>pp_three_rr_alt <span class="ot">&lt;-</span> <span class="fu">predictions</span>(three_rr_alt,</span>
<span id="cb29-16"><a href="#cb29-16" aria-hidden="true" tabindex="-1"></a>  <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&quot;gndr_fct&quot;</span>, <span class="st">&quot;cohort_alt_fct&quot;</span>, <span class="st">&quot;edu_fct&quot;</span>),</span>
<span id="cb29-17"><a href="#cb29-17" aria-hidden="true" tabindex="-1"></a>  <span class="at">newdata =</span> <span class="fu">datagrid</span>(</span>
<span id="cb29-18"><a href="#cb29-18" aria-hidden="true" tabindex="-1"></a>    <span class="at">gndr_fct =</span> <span class="fu">c</span>(<span class="st">&quot;men&quot;</span>, <span class="st">&quot;women&quot;</span>),</span>
<span id="cb29-19"><a href="#cb29-19" aria-hidden="true" tabindex="-1"></a>    <span class="at">cohort_alt_fct =</span> <span class="fu">c</span>(</span>
<span id="cb29-20"><a href="#cb29-20" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;-1929&quot;</span>,</span>
<span id="cb29-21"><a href="#cb29-21" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;1930s&quot;</span>,</span>
<span id="cb29-22"><a href="#cb29-22" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;1940s&quot;</span>,</span>
<span id="cb29-23"><a href="#cb29-23" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;1950s&quot;</span>,</span>
<span id="cb29-24"><a href="#cb29-24" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;1960s&quot;</span>,</span>
<span id="cb29-25"><a href="#cb29-25" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;1970s&quot;</span>,</span>
<span id="cb29-26"><a href="#cb29-26" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;1980s&quot;</span>,</span>
<span id="cb29-27"><a href="#cb29-27" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;1990+&quot;</span></span>
<span id="cb29-28"><a href="#cb29-28" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb29-29"><a href="#cb29-29" aria-hidden="true" tabindex="-1"></a>    <span class="at">edu_fct =</span> <span class="fu">c</span>(</span>
<span id="cb29-30"><a href="#cb29-30" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;(below) lower secondary&quot;</span>,</span>
<span id="cb29-31"><a href="#cb29-31" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;upper secondary&quot;</span>,</span>
<span id="cb29-32"><a href="#cb29-32" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;tertiary&quot;</span></span>
<span id="cb29-33"><a href="#cb29-33" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb29-34"><a href="#cb29-34" aria-hidden="true" tabindex="-1"></a>    <span class="at">grid_type =</span> <span class="st">&quot;counterfactual&quot;</span></span>
<span id="cb29-35"><a href="#cb29-35" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb29-36"><a href="#cb29-36" aria-hidden="true" tabindex="-1"></a>    <span class="at">re.form =</span> <span class="cn">NA</span></span>
<span id="cb29-37"><a href="#cb29-37" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb29-38"><a href="#cb29-38" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb29-39"><a href="#cb29-39" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb29-40"><a href="#cb29-40" aria-hidden="true" tabindex="-1"></a><span class="co"># plot</span></span>
<span id="cb29-41"><a href="#cb29-41" aria-hidden="true" tabindex="-1"></a>plot_pp_three_rr_alt <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(</span>
<span id="cb29-42"><a href="#cb29-42" aria-hidden="true" tabindex="-1"></a>  <span class="at">data =</span> pp_three_rr_alt,</span>
<span id="cb29-43"><a href="#cb29-43" aria-hidden="true" tabindex="-1"></a>  <span class="fu">aes</span>(</span>
<span id="cb29-44"><a href="#cb29-44" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> cohort_alt_fct, <span class="at">y =</span> estimate, <span class="at">ymin =</span> conf.low,</span>
<span id="cb29-45"><a href="#cb29-45" aria-hidden="true" tabindex="-1"></a>    <span class="at">ymax =</span> conf.high, <span class="at">color =</span> edu_fct</span>
<span id="cb29-46"><a href="#cb29-46" aria-hidden="true" tabindex="-1"></a>  )</span>
<span id="cb29-47"><a href="#cb29-47" aria-hidden="true" tabindex="-1"></a>) <span class="sc">+</span></span>
<span id="cb29-48"><a href="#cb29-48" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept =</span> <span class="dv">0</span>, <span class="at">color =</span> <span class="st">&quot;grey70&quot;</span>) <span class="sc">+</span></span>
<span id="cb29-49"><a href="#cb29-49" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_pointrange</span>(<span class="at">position =</span> <span class="fu">position_dodge</span>(<span class="at">width =</span> <span class="fu">c</span>(<span class="fl">0.2</span>))) <span class="sc">+</span></span>
<span id="cb29-50"><a href="#cb29-50" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb29-51"><a href="#cb29-51" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> <span class="cn">NULL</span>, <span class="at">y =</span> <span class="st">&quot;Predicted probability&quot;</span>,</span>
<span id="cb29-52"><a href="#cb29-52" aria-hidden="true" tabindex="-1"></a>    <span class="at">color =</span> <span class="cn">NULL</span></span>
<span id="cb29-53"><a href="#cb29-53" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb29-54"><a href="#cb29-54" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_viridis_d</span>(<span class="at">option =</span> <span class="st">&quot;plasma&quot;</span>, <span class="at">end =</span> .<span class="dv">8</span>) <span class="sc">+</span></span>
<span id="cb29-55"><a href="#cb29-55" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_light</span>() <span class="sc">+</span></span>
<span id="cb29-56"><a href="#cb29-56" aria-hidden="true" tabindex="-1"></a>  <span class="fu">my_theme</span>() <span class="sc">+</span></span>
<span id="cb29-57"><a href="#cb29-57" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;top&quot;</span>) <span class="sc">+</span></span>
<span id="cb29-58"><a href="#cb29-58" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>gndr_fct)</span>
<span id="cb29-59"><a href="#cb29-59" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb29-60"><a href="#cb29-60" aria-hidden="true" tabindex="-1"></a><span class="co"># marginal effects</span></span>
<span id="cb29-61"><a href="#cb29-61" aria-hidden="true" tabindex="-1"></a>me_three_rr_alt <span class="ot">&lt;-</span> <span class="fu">avg_slopes</span>(</span>
<span id="cb29-62"><a href="#cb29-62" aria-hidden="true" tabindex="-1"></a>  three_rr_alt,</span>
<span id="cb29-63"><a href="#cb29-63" aria-hidden="true" tabindex="-1"></a>  <span class="at">variables =</span> <span class="st">&quot;edu_fct&quot;</span>,</span>
<span id="cb29-64"><a href="#cb29-64" aria-hidden="true" tabindex="-1"></a>  <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&quot;cohort_alt_fct&quot;</span>, <span class="st">&quot;gndr_fct&quot;</span>),</span>
<span id="cb29-65"><a href="#cb29-65" aria-hidden="true" tabindex="-1"></a>  <span class="at">newdata =</span> <span class="fu">datagrid</span>(</span>
<span id="cb29-66"><a href="#cb29-66" aria-hidden="true" tabindex="-1"></a>    <span class="at">gndr_fct =</span> <span class="fu">c</span>(<span class="st">&quot;men&quot;</span>, <span class="st">&quot;women&quot;</span>),</span>
<span id="cb29-67"><a href="#cb29-67" aria-hidden="true" tabindex="-1"></a>    <span class="at">cohort_alt_fct =</span> <span class="fu">c</span>(</span>
<span id="cb29-68"><a href="#cb29-68" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;-1929&quot;</span>,</span>
<span id="cb29-69"><a href="#cb29-69" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;1930s&quot;</span>,</span>
<span id="cb29-70"><a href="#cb29-70" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;1940s&quot;</span>,</span>
<span id="cb29-71"><a href="#cb29-71" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;1950s&quot;</span>,</span>
<span id="cb29-72"><a href="#cb29-72" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;1960s&quot;</span>,</span>
<span id="cb29-73"><a href="#cb29-73" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;1970s&quot;</span>,</span>
<span id="cb29-74"><a href="#cb29-74" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;1980s&quot;</span>,</span>
<span id="cb29-75"><a href="#cb29-75" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;1990+&quot;</span></span>
<span id="cb29-76"><a href="#cb29-76" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb29-77"><a href="#cb29-77" aria-hidden="true" tabindex="-1"></a>    <span class="at">edu_fct =</span> <span class="fu">c</span>(</span>
<span id="cb29-78"><a href="#cb29-78" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;(below) lower secondary&quot;</span>,</span>
<span id="cb29-79"><a href="#cb29-79" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;upper secondary&quot;</span>,</span>
<span id="cb29-80"><a href="#cb29-80" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;tertiary&quot;</span></span>
<span id="cb29-81"><a href="#cb29-81" aria-hidden="true" tabindex="-1"></a>    ),</span>
<span id="cb29-82"><a href="#cb29-82" aria-hidden="true" tabindex="-1"></a>    <span class="at">grid_type =</span> <span class="st">&quot;counterfactual&quot;</span></span>
<span id="cb29-83"><a href="#cb29-83" aria-hidden="true" tabindex="-1"></a>  ),</span>
<span id="cb29-84"><a href="#cb29-84" aria-hidden="true" tabindex="-1"></a>    <span class="at">re.form =</span> <span class="cn">NA</span></span>
<span id="cb29-85"><a href="#cb29-85" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb29-86"><a href="#cb29-86" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb29-87"><a href="#cb29-87" aria-hidden="true" tabindex="-1"></a>me_three_rr_alt <span class="ot">&lt;-</span> me_three_rr_alt <span class="sc">%&gt;%</span></span>
<span id="cb29-88"><a href="#cb29-88" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">contrast =</span> <span class="fu">case_when</span>(</span>
<span id="cb29-89"><a href="#cb29-89" aria-hidden="true" tabindex="-1"></a>    contrast <span class="sc">==</span> <span class="st">&quot;tertiary - (below) lower secondary&quot;</span> <span class="sc">~</span></span>
<span id="cb29-90"><a href="#cb29-90" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;high vs low edu&quot;</span>,</span>
<span id="cb29-91"><a href="#cb29-91" aria-hidden="true" tabindex="-1"></a>    contrast <span class="sc">==</span> <span class="st">&quot;upper secondary - (below) lower secondary&quot;</span> <span class="sc">~</span></span>
<span id="cb29-92"><a href="#cb29-92" aria-hidden="true" tabindex="-1"></a>      <span class="st">&quot;medium vs low edu&quot;</span></span>
<span id="cb29-93"><a href="#cb29-93" aria-hidden="true" tabindex="-1"></a>  ))</span>
<span id="cb29-94"><a href="#cb29-94" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb29-95"><a href="#cb29-95" aria-hidden="true" tabindex="-1"></a>plot_me_three_rr_alt <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(<span class="at">data =</span> me_three_rr_alt, <span class="fu">aes</span>(</span>
<span id="cb29-96"><a href="#cb29-96" aria-hidden="true" tabindex="-1"></a>  <span class="at">x =</span> cohort_alt_fct, <span class="at">y =</span> estimate,</span>
<span id="cb29-97"><a href="#cb29-97" aria-hidden="true" tabindex="-1"></a>  <span class="at">ymin =</span> conf.low, <span class="at">ymax =</span> conf.high,</span>
<span id="cb29-98"><a href="#cb29-98" aria-hidden="true" tabindex="-1"></a>  <span class="at">color =</span> contrast</span>
<span id="cb29-99"><a href="#cb29-99" aria-hidden="true" tabindex="-1"></a>)) <span class="sc">+</span></span>
<span id="cb29-100"><a href="#cb29-100" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept =</span> <span class="dv">0</span>, <span class="at">color =</span> <span class="st">&quot;grey70&quot;</span>) <span class="sc">+</span></span>
<span id="cb29-101"><a href="#cb29-101" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_pointrange</span>(<span class="at">position =</span> <span class="fu">position_dodge</span>(<span class="at">width =</span> <span class="fu">c</span>(<span class="fl">0.2</span>))) <span class="sc">+</span></span>
<span id="cb29-102"><a href="#cb29-102" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb29-103"><a href="#cb29-103" aria-hidden="true" tabindex="-1"></a>    <span class="at">x =</span> <span class="cn">NULL</span>, <span class="at">color =</span> <span class="st">&quot;Education&quot;</span>,</span>
<span id="cb29-104"><a href="#cb29-104" aria-hidden="true" tabindex="-1"></a>    <span class="at">y =</span> <span class="st">&quot;Marginal effect of education&quot;</span></span>
<span id="cb29-105"><a href="#cb29-105" aria-hidden="true" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb29-106"><a href="#cb29-106" aria-hidden="true" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;#fe9f6d&quot;</span>, <span class="st">&quot;#8c2981&quot;</span>)) <span class="sc">+</span></span>
<span id="cb29-107"><a href="#cb29-107" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>gndr_fct) <span class="sc">+</span></span>
<span id="cb29-108"><a href="#cb29-108" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme_light</span>() <span class="sc">+</span></span>
<span id="cb29-109"><a href="#cb29-109" aria-hidden="true" tabindex="-1"></a>  <span class="fu">my_theme</span>() <span class="sc">+</span></span>
<span id="cb29-110"><a href="#cb29-110" aria-hidden="true" tabindex="-1"></a>  <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">&quot;top&quot;</span>)</span>
<span id="cb29-111"><a href="#cb29-111" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb29-112"><a href="#cb29-112" aria-hidden="true" tabindex="-1"></a><span class="co"># Combine the plots vertically</span></span>
<span id="cb29-113"><a href="#cb29-113" aria-hidden="true" tabindex="-1"></a>combined_plot <span class="ot">&lt;-</span> plot_pp_three_rr_alt <span class="sc">/</span> plot_me_three_rr_alt</span>
<span id="cb29-114"><a href="#cb29-114" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb29-115"><a href="#cb29-115" aria-hidden="true" tabindex="-1"></a><span class="co"># Save the combined plot</span></span>
<span id="cb29-116"><a href="#cb29-116" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(</span>
<span id="cb29-117"><a href="#cb29-117" aria-hidden="true" tabindex="-1"></a>  <span class="at">filename =</span> <span class="fu">here</span>(<span class="st">&quot;figs&quot;</span>, <span class="st">&quot;final&quot;</span>, <span class="st">&quot;appendix_fig_f2.png&quot;</span>),</span>
<span id="cb29-118"><a href="#cb29-118" aria-hidden="true" tabindex="-1"></a>  <span class="at">plot =</span> combined_plot,</span>
<span id="cb29-119"><a href="#cb29-119" aria-hidden="true" tabindex="-1"></a>  <span class="at">units =</span> <span class="fu">c</span>(<span class="st">&quot;in&quot;</span>),</span>
<span id="cb29-120"><a href="#cb29-120" aria-hidden="true" tabindex="-1"></a>  <span class="at">width =</span> <span class="dv">8</span>,</span>
<span id="cb29-121"><a href="#cb29-121" aria-hidden="true" tabindex="-1"></a>  <span class="at">height =</span> <span class="dv">10</span>, <span class="co"># Adjust height to fit two plots</span></span>
<span id="cb29-122"><a href="#cb29-122" aria-hidden="true" tabindex="-1"></a>  <span class="at">dpi =</span> <span class="dv">300</span></span>
<span id="cb29-123"><a href="#cb29-123" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb29-124"><a href="#cb29-124" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb29-125"><a href="#cb29-125" aria-hidden="true" tabindex="-1"></a>combined_plot</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
</details>
<div class="cell-output-display">
<div id="fig-fig_f2" class="quarto-float quarto-figure quarto-figure-center anchored">
<figure class="quarto-float quarto-float-fig figure">
<div aria-describedby="fig-fig_f2-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
<img role="img" 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</div>
<figcaption class="quarto-float-caption-bottom quarto-float-caption quarto-float-fig" id="fig-fig_f2-caption-0ceaefa1-69ba-4598-a22c-09a6ac19f8ca">
Figure 20: Voting for a green party by cohort and education (three-way interaction)
</figcaption>
</figure>
</div>
</div>
</div>
</section>
</section>

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});
</script>
</div> <!-- /content -->




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